2.69 score from hupso.pl for:
dbms2.com



HTML Content


Titledbms 2 : database management and analytic technologies in a changing world

Length: 74, Words: 10
Description a blog about dbms (database management) and analytic technologies -- especially disruptive ones -- by curt monash, the industry's premier analyst.

Length: 146, Words: 21
Keywords dbms, database, business intelligence, bi, data warehouse, relational database management system
Robots follow, all
Charset UTF-8
Og Meta - Title pusty
Og Meta - Description pusty
Og Meta - Site name pusty
Tytuł powinien zawierać pomiędzy 10 a 70 znaków (ze spacjami), a mniej niż 12 słów w długości.
Meta opis powinien zawierać pomiędzy 50 a 160 znaków (łącznie ze spacjami), a mniej niż 24 słów w długości.
Kodowanie znaków powinny być określone , UTF-8 jest chyba najlepszy zestaw znaków, aby przejść z powodu UTF-8 jest bardziej międzynarodowy kodowaniem.
Otwarte obiekty wykresu powinny być obecne w stronie internetowej (więcej informacji na temat protokołu OpenGraph: http://ogp.me/)

SEO Content

Words/Characters 3149
Text/HTML 26.93 %
Headings H1 10
H2 9
H3 0
H4 0
H5 0
H6 0
H1
dbas of the future
mongodb 3.4 and “multimodel” query
rapid analytics
notes on anomaly management
notes on the transition to the cloud
“real-time” is getting real
are analytic rdbms and data warehouse appliances obsolete?
introduction to data artisans and flink
more about databricks and spark
notes on datastax and cassandra
H2
search our blogs and white papers
monash research blogs
user consulting
vendor advisory
monash research highlights
recent posts
categories
date archives
admin
H3
H4
H5
H6
strong
dbas
database design
manage production databases.
view
streaming.
low-latency ingest
business intelligence
advanced analytics
alerting,
identify, understand, avert
remediate bad stuff,
“insights”
public cloud.
saas.
colo.
private cloud.
hybrid cloud,
true on-prem(ises).
interactive computer response
“true” real-time vs. near-real-time
pointless.
machines race other machines,
how fresh does data need to be?
as fresh as is needed to support the best decision-making.
analytic rdbms
are still great for hard-core business intelligence,
but they aren’t good for much else.
b
i
em dbas
database design
manage production databases.
view
streaming.
low-latency ingest
business intelligence
advanced analytics
alerting,
identify, understand, avert
remediate bad stuff,
“insights”
public cloud.
saas.
colo.
private cloud.
hybrid cloud,
true on-prem(ises).
interactive computer response
“true” real-time vs. near-real-time
pointless.
machines race other machines,
how fresh does data need to be?
as fresh as is needed to support the best decision-making.
analytic rdbms
are still great for hard-core business intelligence,
but they aren’t good for much else.
Bolds strong 27
b 0
i 0
em 27
Zawartość strony internetowej powinno zawierać więcej niż 250 słów, z stopa tekst / kod jest wyższy niż 20%.
Pozycji używać znaczników (h1, h2, h3, ...), aby określić temat sekcji lub ustępów na stronie, ale zwykle, użyj mniej niż 6 dla każdego tagu pozycje zachować swoją stronę zwięzły.
Styl używać silnych i kursywy znaczniki podkreślić swoje słowa kluczowe swojej stronie, ale nie nadużywać (mniej niż 16 silnych tagi i 16 znaczników kursywy)

Statystyki strony

twitter:title pusty
twitter:description pusty
google+ itemprop=name pusty
Pliki zewnętrzne 2
Pliki CSS 0
Pliki javascript 2
Plik należy zmniejszyć całkowite odwołanie plików (CSS + JavaScript) do 7-8 maksymalnie.

Linki wewnętrzne i zewnętrzne

Linki 416
Linki wewnętrzne 0
Linki zewnętrzne 416
Linki bez atrybutu Title 406
Linki z atrybutem NOFOLLOW 0
Linki - Użyj atrybutu tytuł dla każdego łącza. Nofollow link jest link, który nie pozwala wyszukiwarkom boty zrealizują są odnośniki no follow. Należy zwracać uwagę na ich użytkowania

Linki wewnętrzne

pusty

Linki zewnętrzne

- http://www.dbms2.com/
- http://www.monash.com
home http://www.dbms2.com
about http://www.dbms2.com/database-blog/
contact http://www.monash.com/contact.html
feeds http://www.monash.com/signup.html
dbas of the future http://www.dbms2.com/2016/11/23/dbas-of-the-future/
july visit to datastax http://www.dbms2.com/2016/07/19/notes-from-a-long-trip-july-19-2016/
refactored http://www.dbms2.com/2013/07/20/the-refactoring-of-everything/
data mustering http://www.dbms2.com/2011/11/28/terminology-data-mustering/
mongodb 3.4 http://www.dbms2.com/2016/11/23/mongodb-3-4-and-multimodel-query/
read more http://www.dbms2.com/2016/11/23/dbas-of-the-future/#more-10140
databricks, spark and bdas http://www.dbms2.com/category/products-and-vendors/databricks-bdas-spark-shark-berkeley-amplab/
hadoop http://www.dbms2.com/category/products-and-vendors/hadoop-products-and-vendors/
mongodb http://www.dbms2.com/category/products-and-vendors/10gen-mongodb/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
streaming and complex event processing (cep) http://www.dbms2.com/category/memory-centric-data-management/event-stream-processing/
leave a comment http://www.dbms2.com/2016/11/23/dbas-of-the-future/#respond
mongodb 3.4 and “multimodel” query http://www.dbms2.com/2016/11/23/mongodb-3-4-and-multimodel-query/
read more http://www.dbms2.com/2016/11/23/mongodb-3-4-and-multimodel-query/#more-10137
database diversity http://www.dbms2.com/category/database-theory-practice/database-diversity/
emulation, transparency, portability http://www.dbms2.com/category/database-emulation-transparency-portability-compatibility/
mongodb http://www.dbms2.com/category/products-and-vendors/10gen-mongodb/
mysql http://www.dbms2.com/category/products-and-vendors/mysql/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
open source http://www.dbms2.com/category/database-management-system/open-source/
rdf and graphs http://www.dbms2.com/category/datatype/rdf-graph-database/
structured documents http://www.dbms2.com/category/datatype/native-xml-database/
text http://www.dbms2.com/category/datatype/text-search/
leave a comment http://www.dbms2.com/2016/11/23/mongodb-3-4-and-multimodel-query/#respond
rapid analytics http://www.dbms2.com/2016/10/21/rapid-analytics/
“real-time” is getting real http://www.dbms2.com/2016/09/06/real-time-is-getting-real/
flink http://www.dbms2.com/2016/08/21/introduction-to-data-artisans-and-flink/
anomaly management http://www.dbms2.com/2016/10/10/notes-on-anomaly-management/
alerting http://www.dbms2.com/2010/07/25/alerts-metrics-dashboards/
investigative analytics http://www.dbms2.com/2011/03/03/investigative-analytics/
read more http://www.dbms2.com/2016/10/21/rapid-analytics/#more-10122
business intelligence http://www.dbms2.com/category/analytics-technologies/business-intelligence/
databricks, spark and bdas http://www.dbms2.com/category/products-and-vendors/databricks-bdas-spark-shark-berkeley-amplab/
in-memory dbms http://www.dbms2.com/category/memory-centric-data-management/in-memory-dbms/
investment research and trading http://www.dbms2.com/category/applications/algorithmic-trading-investment-research/
log analysis http://www.dbms2.com/category/applications/log-file-logfile-analysis/
streaming and complex event processing (cep) http://www.dbms2.com/category/memory-centric-data-management/event-stream-processing/
text http://www.dbms2.com/category/datatype/text-search/
web analytics http://www.dbms2.com/category/applications/web-analytics/
zoomdata http://www.dbms2.com/category/products-and-vendors/zoomdata/
2 comments http://www.dbms2.com/2016/10/21/rapid-analytics/#comments
notes on anomaly management http://www.dbms2.com/2016/10/10/notes-on-anomaly-management/
anomaly management is hard http://www.dbms2.com/2016/06/05/challenges-in-anomaly-management/
artificial intelligence http://www.dbms2.com/2015/12/01/what-is-ai-and-who-has-it/
read more http://www.dbms2.com/2016/10/10/notes-on-anomaly-management/#more-10103
business intelligence http://www.dbms2.com/category/analytics-technologies/business-intelligence/
log analysis http://www.dbms2.com/category/applications/log-file-logfile-analysis/
predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
web analytics http://www.dbms2.com/category/applications/web-analytics/
leave a comment http://www.dbms2.com/2016/10/10/notes-on-anomaly-management/#respond
notes on the transition to the cloud http://www.dbms2.com/2016/10/03/notes-on-the-transition-to-the-cloud/
monash’s laws of commercial semantics http://www.strategicmessaging.com/monashs-first-law-of-commercial-semantics-explained/2009/01/09/
read more http://www.dbms2.com/2016/10/03/notes-on-the-transition-to-the-cloud/#more-10098
amazon and its cloud http://www.dbms2.com/category/products-and-vendors/amazon-simpledb-s3-ec2/
cloud computing http://www.dbms2.com/category/software-as-a-service-database-saas/cloud-computing/
data mart outsourcing http://www.dbms2.com/category/analytics-technologies/data-mart-warehouse-outsourcing/
data warehousing http://www.dbms2.com/category/analytics-technologies/data-warehouse/
database diversity http://www.dbms2.com/category/database-theory-practice/database-diversity/
google http://www.dbms2.com/category/products-and-vendors/google-mapreduce-bigtable/
hadoop http://www.dbms2.com/category/products-and-vendors/hadoop-products-and-vendors/
health care http://www.dbms2.com/category/applications/health-care-medical/
investment research and trading http://www.dbms2.com/category/applications/algorithmic-trading-investment-research/
log analysis http://www.dbms2.com/category/applications/log-file-logfile-analysis/
microsoft and sql*server http://www.dbms2.com/category/products-and-vendors/microsoft-sqlserver/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
software as a service (saas) http://www.dbms2.com/category/software-as-a-service-database-saas/
solid-state memory http://www.dbms2.com/category/storage/solid-state-memory-disk-flash/
splunk http://www.dbms2.com/category/products-and-vendors/splunk/
surveillance and privacy http://www.dbms2.com/category/technology-public-policy/privacy-surveillance-liberty/
text http://www.dbms2.com/category/datatype/text-search/
web analytics http://www.dbms2.com/category/applications/web-analytics/
3 comments http://www.dbms2.com/2016/10/03/notes-on-the-transition-to-the-cloud/#comments
“real-time” is getting real http://www.dbms2.com/2016/09/06/real-time-is-getting-real/
high-performance analytic rdbms http://www.dbms2.com/2016/08/28/are-analytic-rdbms-and-data-warehouse-appliances-obsolete/
human users’ perceptions of speed http://www.dbms2.com/2012/04/05/human-real-time/
read more http://www.dbms2.com/2016/09/06/real-time-is-getting-real/#more-10083
business intelligence http://www.dbms2.com/category/analytics-technologies/business-intelligence/
data warehousing http://www.dbms2.com/category/analytics-technologies/data-warehouse/
eai, eii, etl, elt, etlt http://www.dbms2.com/category/data-integration-middleware/data-etl-etl-etlt-eai-eii/
in-memory dbms http://www.dbms2.com/category/memory-centric-data-management/in-memory-dbms/
investment research and trading http://www.dbms2.com/category/applications/algorithmic-trading-investment-research/
log analysis http://www.dbms2.com/category/applications/log-file-logfile-analysis/
memsql http://www.dbms2.com/category/products-and-vendors/memsql/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
streaming and complex event processing (cep) http://www.dbms2.com/category/memory-centric-data-management/event-stream-processing/
web analytics http://www.dbms2.com/category/applications/web-analytics/
3 comments http://www.dbms2.com/2016/09/06/real-time-is-getting-real/#comments
are analytic rdbms and data warehouse appliances obsolete? http://www.dbms2.com/2016/08/28/are-analytic-rdbms-and-data-warehouse-appliances-obsolete/
ibm bought netezza http://www.dbms2.com/2010/09/20/ibm-netezza-acquisition/
microsoft bought datallegro http://www.dbms2.com/2008/07/24/microsoft-is-buying-datallegro/
hp bought vertica http://www.dbms2.com/2011/02/14/some-quick-notes-on-hp-vertica/
greenplum went to emc/vmware/pivotal http://www.dbms2.com/2010/07/06/emc-is-buying-greenplum/
actian bought both paraccel and vectorwise http://www.dbms2.com/2013/04/25/goodbye-vectorwise-farewell-paraccel/
blu http://www.dbms2.com/2013/05/27/ibm-blu/
open-sourced greenplum http://www.dbms2.com/2015/02/18/greenplum-is-being-open-sourced/
teradata claimed a few large aster accounts http://www.dbms2.com/2014/08/31/notes-from-a-visit-to-teradata/
hadapt http://www.dbms2.com/2014/07/23/teradata-bought-hadapt-and-revelytix/
kickfire http://www.dbms2.com/2010/08/12/teradata-future-product-strategy/
infinidb http://www.dbms2.com/2009/11/07/calponts-infinidb/
impala http://www.dbms2.com/2012/11/01/more-on-cloudera-impala/
hadoop-based alternatives http://www.dbms2.com/category/database-theory-practice/sqlhadoop-integration/
read more http://www.dbms2.com/2016/08/28/are-analytic-rdbms-and-data-warehouse-appliances-obsolete/#more-10078
actian and ingres http://www.dbms2.com/category/products-and-vendors/ingres/
amazon and its cloud http://www.dbms2.com/category/products-and-vendors/amazon-simpledb-s3-ec2/
aster data http://www.dbms2.com/category/products-and-vendors/aster-data-warehouse/
business intelligence http://www.dbms2.com/category/analytics-technologies/business-intelligence/
calpont http://www.dbms2.com/category/products-and-vendors/calpont/
cloud computing http://www.dbms2.com/category/software-as-a-service-database-saas/cloud-computing/
data warehouse appliances http://www.dbms2.com/category/database-management-system/data-warehouse-appliances/
data warehousing http://www.dbms2.com/category/analytics-technologies/data-warehouse/
databricks, spark and bdas http://www.dbms2.com/category/products-and-vendors/databricks-bdas-spark-shark-berkeley-amplab/
datallegro http://www.dbms2.com/category/products-and-vendors/datallegro/
eai, eii, etl, elt, etlt http://www.dbms2.com/category/data-integration-middleware/data-etl-etl-etlt-eai-eii/
emc http://www.dbms2.com/category/products-and-vendors/emc/
exasol http://www.dbms2.com/category/products-and-vendors/exasol/
greenplum http://www.dbms2.com/category/products-and-vendors/greenplum/
hadapt http://www.dbms2.com/category/products-and-vendors/hadapt/
hadoop http://www.dbms2.com/category/products-and-vendors/hadoop-products-and-vendors/
hp and neoview http://www.dbms2.com/category/products-and-vendors/hp-and-neoview/
ibm and db2 http://www.dbms2.com/category/products-and-vendors/ibm-and-db2/
in-memory dbms http://www.dbms2.com/category/memory-centric-data-management/in-memory-dbms/
infobright http://www.dbms2.com/category/products-and-vendors/infobright-brighthouse/
kickfire http://www.dbms2.com/category/products-and-vendors/kickfire/
kognitio http://www.dbms2.com/category/products-and-vendors/kognitio/
memsql http://www.dbms2.com/category/products-and-vendors/memsql/
microsoft and sql*server http://www.dbms2.com/category/products-and-vendors/microsoft-sqlserver/
netezza http://www.dbms2.com/category/products-and-vendors/netezza/
open source http://www.dbms2.com/category/database-management-system/open-source/
oracle http://www.dbms2.com/category/products-and-vendors/oracle/
paraccel http://www.dbms2.com/category/products-and-vendors/paraccel/
predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
sap ag http://www.dbms2.com/category/products-and-vendors/sap-bia-maxdb/
sql/hadoop integration http://www.dbms2.com/category/database-theory-practice/sqlhadoop-integration/
sybase http://www.dbms2.com/category/products-and-vendors/sybase/
teradata http://www.dbms2.com/category/products-and-vendors/teradata/
vectorwise http://www.dbms2.com/category/products-and-vendors/vectorwise/
vertica systems http://www.dbms2.com/category/products-and-vendors/vertica-systems/
27 comments http://www.dbms2.com/2016/08/28/are-analytic-rdbms-and-data-warehouse-appliances-obsolete/#comments
introduction to data artisans and flink http://www.dbms2.com/2016/08/21/introduction-to-data-artisans-and-flink/
google paper https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43864.pdf
read more http://www.dbms2.com/2016/08/21/introduction-to-data-artisans-and-flink/#more-10063
cloudera http://www.dbms2.com/category/products-and-vendors/cloudera/
databricks, spark and bdas http://www.dbms2.com/category/products-and-vendors/databricks-bdas-spark-shark-berkeley-amplab/
eai, eii, etl, elt, etlt http://www.dbms2.com/category/data-integration-middleware/data-etl-etl-etlt-eai-eii/
hadoop http://www.dbms2.com/category/products-and-vendors/hadoop-products-and-vendors/
hortonworks http://www.dbms2.com/category/products-and-vendors/hortonworks/
intel http://www.dbms2.com/category/products-and-vendors/intel/
market share and customer counts http://www.dbms2.com/category/market-share/
open source http://www.dbms2.com/category/database-management-system/open-source/
streaming and complex event processing (cep) http://www.dbms2.com/category/memory-centric-data-management/event-stream-processing/
3 comments http://www.dbms2.com/2016/08/21/introduction-to-data-artisans-and-flink/#comments
more about databricks and spark http://www.dbms2.com/2016/08/21/more-about-databricks-and-spark/
recent post about databricks http://www.dbms2.com/2016/07/31/notes-on-spark-and-databricks-generalities/
read more http://www.dbms2.com/2016/08/21/more-about-databricks-and-spark/#more-10064
data warehousing http://www.dbms2.com/category/analytics-technologies/data-warehouse/
databricks, spark and bdas http://www.dbms2.com/category/products-and-vendors/databricks-bdas-spark-shark-berkeley-amplab/
investment research and trading http://www.dbms2.com/category/applications/algorithmic-trading-investment-research/
market share and customer counts http://www.dbms2.com/category/market-share/
open source http://www.dbms2.com/category/database-management-system/open-source/
predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
software as a service (saas) http://www.dbms2.com/category/software-as-a-service-database-saas/
specific users http://www.dbms2.com/category/users/
streaming and complex event processing (cep) http://www.dbms2.com/category/memory-centric-data-management/event-stream-processing/
web analytics http://www.dbms2.com/category/applications/web-analytics/
3 comments http://www.dbms2.com/2016/08/21/more-about-databricks-and-spark/#comments
notes on datastax and cassandra http://www.dbms2.com/2016/08/07/notes-on-datastax-and-cassandra/
nosql dbas http://www.dbms2.com/2016/07/19/notes-from-a-long-trip-july-19-2016/
fear of vendor lock-in http://www.dbms2.com/2016/07/19/notes-on-vendor-lock-in/
microsoft is a datastax customer http://www.b2bnn.com/2015/09/how-microsoft-truly-uses-spark-and-cassandra-for-big-data-analytics/
read more http://www.dbms2.com/2016/08/07/notes-on-datastax-and-cassandra/#more-10057
cassandra http://www.dbms2.com/category/products-and-vendors/cassandra-products-and-vendors/
datastax http://www.dbms2.com/category/products-and-vendors/riptano/
microsoft and sql*server http://www.dbms2.com/category/products-and-vendors/microsoft-sqlserver/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
specific users http://www.dbms2.com/category/users/
2 comments http://www.dbms2.com/2016/08/07/notes-on-datastax-and-cassandra/#comments
next page → http://www.dbms2.com/page/2/
- http://feeds.feedburner.com/monashinformationservices?format=xml
rss http://feeds.feedburner.com/monashinformationservices?format=xml
login http://www.dbms2.com/wp-admin/
dbms 2 http://www.dbms2.com
text technologies http://www.texttechnologies.com
strategic messaging http://www.strategicmessaging.com
the monash report http://www.monashreport.com
software memories http://www.softwarememories.com
we can help. http://www.monash.com/adviseusers.html
what they should do about it http://www.monash.com/advisevendors.html
rss or email http://www.monash.com/signup.html
dbas of the future http://www.dbms2.com/2016/11/23/dbas-of-the-future/
mongodb 3.4 and “multimodel” query http://www.dbms2.com/2016/11/23/mongodb-3-4-and-multimodel-query/
rapid analytics http://www.dbms2.com/2016/10/21/rapid-analytics/
notes on anomaly management http://www.dbms2.com/2016/10/10/notes-on-anomaly-management/
notes on the transition to the cloud http://www.dbms2.com/2016/10/03/notes-on-the-transition-to-the-cloud/
about this blog http://www.dbms2.com/category/housekeeping/
analytic glossary http://www.dbms2.com/category/analytic-glossary/
analytic technologies http://www.dbms2.com/category/analytics-technologies/
business intelligence http://www.dbms2.com/category/analytics-technologies/business-intelligence/
data mart outsourcing http://www.dbms2.com/category/analytics-technologies/data-mart-warehouse-outsourcing/
data warehousing http://www.dbms2.com/category/analytics-technologies/data-warehouse/
molap http://www.dbms2.com/category/analytics-technologies/molap/
predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
application areas http://www.dbms2.com/category/applications/
games and virtual worlds http://www.dbms2.com/category/applications/games-virtual-worlds/
health care http://www.dbms2.com/category/applications/health-care-medical/
investment research and trading http://www.dbms2.com/category/applications/algorithmic-trading-investment-research/
log analysis http://www.dbms2.com/category/applications/log-file-logfile-analysis/
scientific research http://www.dbms2.com/category/applications/scientific-research/
telecommunications http://www.dbms2.com/category/applications/telecommunications/
web analytics http://www.dbms2.com/category/applications/web-analytics/
buying processes http://www.dbms2.com/category/buying-processes/
benchmarks and pocs http://www.dbms2.com/category/buying-processes/benchmarks-and-pocs/
companies and products http://www.dbms2.com/category/products-and-vendors/
1010data http://www.dbms2.com/category/products-and-vendors/1010data/
ab initio software http://www.dbms2.com/category/products-and-vendors/ab-initio-software-corporation/
actian and ingres http://www.dbms2.com/category/products-and-vendors/ingres/
aerospike http://www.dbms2.com/category/products-and-vendors/citrusleaf-aerospike/
akiban http://www.dbms2.com/category/products-and-vendors/akiban/
aleri and coral8 http://www.dbms2.com/category/products-and-vendors/coral8/
algebraix http://www.dbms2.com/category/products-and-vendors/algebraix/
alpha five http://www.dbms2.com/category/products-and-vendors/alpha-five/
amazon and its cloud http://www.dbms2.com/category/products-and-vendors/amazon-simpledb-s3-ec2/
ants software http://www.dbms2.com/category/products-and-vendors/ants/
aster data http://www.dbms2.com/category/products-and-vendors/aster-data-warehouse/
ayasdi http://www.dbms2.com/category/products-and-vendors/ayasdi/
basho and riak http://www.dbms2.com/category/products-and-vendors/basho-riak/
business objects http://www.dbms2.com/category/products-and-vendors/business-objects/
calpont http://www.dbms2.com/category/products-and-vendors/calpont/
cassandra http://www.dbms2.com/category/products-and-vendors/cassandra-products-and-vendors/
cast iron systems http://www.dbms2.com/category/products-and-vendors/cast-iron-systems/
cirro http://www.dbms2.com/category/products-and-vendors/cirro/
citus data http://www.dbms2.com/category/products-and-vendors/citus-data-citusdb/
clearstory data http://www.dbms2.com/category/products-and-vendors/clearstory-data/
cloudant http://www.dbms2.com/category/products-and-vendors/cloudant/
cloudera http://www.dbms2.com/category/products-and-vendors/cloudera/
clustrix http://www.dbms2.com/category/products-and-vendors/clustrix/
cogito and 7 degrees http://www.dbms2.com/category/products-and-vendors/7-degrees-cogito/
cognos http://www.dbms2.com/category/products-and-vendors/cognos-applix-tm1/
continuent http://www.dbms2.com/category/products-and-vendors/continuent-tungsten/
couchbase http://www.dbms2.com/category/products-and-vendors/northscale-membase/
couchdb http://www.dbms2.com/category/products-and-vendors/couchdb/
databricks, spark and bdas http://www.dbms2.com/category/products-and-vendors/databricks-bdas-spark-shark-berkeley-amplab/
datallegro http://www.dbms2.com/category/products-and-vendors/datallegro/
datameer http://www.dbms2.com/category/products-and-vendors/datameer/
datastax http://www.dbms2.com/category/products-and-vendors/riptano/
dataupia http://www.dbms2.com/category/products-and-vendors/dataupia/
dbshards and codefutures http://www.dbms2.com/category/products-and-vendors/dbshards-code-futures/
elastra http://www.dbms2.com/category/products-and-vendors/elastra/
emc http://www.dbms2.com/category/products-and-vendors/emc/
endeca http://www.dbms2.com/category/products-and-vendors/endeca/
enterprisedb and postgres plus http://www.dbms2.com/category/products-and-vendors/enterprisedb-postgres-plus/
exasol http://www.dbms2.com/category/products-and-vendors/exasol/
expressor http://www.dbms2.com/category/products-and-vendors/expressor-software/
filemaker http://www.dbms2.com/category/products-and-vendors/filemaker/
geniedb http://www.dbms2.com/category/products-and-vendors/geniedb/
gooddata http://www.dbms2.com/category/products-and-vendors/gooddata/
google http://www.dbms2.com/category/products-and-vendors/google-mapreduce-bigtable/
greenplum http://www.dbms2.com/category/products-and-vendors/greenplum/
groovy corporation http://www.dbms2.com/category/products-and-vendors/groovy-sql-switch/
hadapt http://www.dbms2.com/category/products-and-vendors/hadapt/
hadoop http://www.dbms2.com/category/products-and-vendors/hadoop-products-and-vendors/
hbase http://www.dbms2.com/category/products-and-vendors/hbase/
hortonworks http://www.dbms2.com/category/products-and-vendors/hortonworks/
hp and neoview http://www.dbms2.com/category/products-and-vendors/hp-and-neoview/
ibm and db2 http://www.dbms2.com/category/products-and-vendors/ibm-and-db2/
purexml http://www.dbms2.com/category/products-and-vendors/ibm-and-db2/purexml/
illuminate solutions http://www.dbms2.com/category/products-and-vendors/iluminate/
infobright http://www.dbms2.com/category/products-and-vendors/infobright-brighthouse/
informatica http://www.dbms2.com/category/products-and-vendors/informatica/
information builders http://www.dbms2.com/category/products-and-vendors/information-builders-ibi/
inforsense http://www.dbms2.com/category/products-and-vendors/inforsense/
intel http://www.dbms2.com/category/products-and-vendors/intel/
intersystems and cache' http://www.dbms2.com/category/products-and-vendors/intersystems-cache-ensemble/
jaspersoft http://www.dbms2.com/category/products-and-vendors/jaspersoft/
kafka and confluent http://www.dbms2.com/category/products-and-vendors/confluent-kafka/
kalido http://www.dbms2.com/category/products-and-vendors/kalido/
kaminario http://www.dbms2.com/category/products-and-vendors/kaminario-k2/
kickfire http://www.dbms2.com/category/products-and-vendors/kickfire/
kognitio http://www.dbms2.com/category/products-and-vendors/kognitio/
kxen http://www.dbms2.com/category/products-and-vendors/kxen/
mapr http://www.dbms2.com/category/products-and-vendors/mapr/
marklogic http://www.dbms2.com/category/products-and-vendors/marklogic/
mcobject http://www.dbms2.com/category/products-and-vendors/mcobject-extremedb/
memcached http://www.dbms2.com/category/products-and-vendors/memcached/
memsql http://www.dbms2.com/category/products-and-vendors/memsql/
metamarkets and druid http://www.dbms2.com/category/products-and-vendors/metamarkets-druid/
microsoft and sql*server http://www.dbms2.com/category/products-and-vendors/microsoft-sqlserver/
microstrategy http://www.dbms2.com/category/products-and-vendors/microstrategy/
monetdb http://www.dbms2.com/category/products-and-vendors/monetdb/
mongodb http://www.dbms2.com/category/products-and-vendors/10gen-mongodb/
mysql http://www.dbms2.com/category/products-and-vendors/mysql/
neo technology and neo4j http://www.dbms2.com/category/products-and-vendors/neo4j-neo-technology/
netezza http://www.dbms2.com/category/products-and-vendors/netezza/
nuodb http://www.dbms2.com/category/products-and-vendors/nuodb/
nutonian http://www.dbms2.com/category/products-and-vendors/nutonian/
objectivity and infinite graph http://www.dbms2.com/category/products-and-vendors/objectivity-infinite-graph/
oracle http://www.dbms2.com/category/products-and-vendors/oracle/
exadata http://www.dbms2.com/category/products-and-vendors/oracle/exadata-database-machine/
oracle timesten http://www.dbms2.com/category/products-and-vendors/oracle-timesten/
paraccel http://www.dbms2.com/category/products-and-vendors/paraccel/
pentaho http://www.dbms2.com/category/products-and-vendors/pentaho/
pervasive software http://www.dbms2.com/category/products-and-vendors/pervasive-software/
pivotlink http://www.dbms2.com/category/products-and-vendors/pivotlink/
platfora http://www.dbms2.com/category/products-and-vendors/platfora/
postgresql http://www.dbms2.com/category/products-and-vendors/postgresql/
progress, apama, and datadirect http://www.dbms2.com/category/products-and-vendors/progress-apama-datadirect/
qliktech and qlikview http://www.dbms2.com/category/products-and-vendors/qliktech-and-qlikview/
rainstor http://www.dbms2.com/category/products-and-vendors/clearpace/
revolution analytics http://www.dbms2.com/category/products-and-vendors/revolution-analytics-r/
rocana http://www.dbms2.com/category/products-and-vendors/rocana-scalingdata/
salesforce.com http://www.dbms2.com/category/products-and-vendors/salesforce-force-database-com-heroku/
sand technology http://www.dbms2.com/category/products-and-vendors/sand-technology/
sap ag http://www.dbms2.com/category/products-and-vendors/sap-bia-maxdb/
sas institute http://www.dbms2.com/category/products-and-vendors/sas-institute/
scalebase http://www.dbms2.com/category/products-and-vendors/scalebase/
scaledb http://www.dbms2.com/category/products-and-vendors/scaledb/
schooner information technology http://www.dbms2.com/category/products-and-vendors/schooner-information-technology-appliance/
scidb http://www.dbms2.com/category/products-and-vendors/scidb/
sensage http://www.dbms2.com/category/products-and-vendors/sensage/
snaplogic http://www.dbms2.com/category/products-and-vendors/snaplogic/
software ag http://www.dbms2.com/category/products-and-vendors/adabas-software-ag/
soliddb http://www.dbms2.com/category/products-and-vendors/soliddb/
splunk http://www.dbms2.com/category/products-and-vendors/splunk/
starcounter http://www.dbms2.com/category/products-and-vendors/starcounter/
streambase http://www.dbms2.com/category/products-and-vendors/streambase/
sybase http://www.dbms2.com/category/products-and-vendors/sybase/
syncsort http://www.dbms2.com/category/products-and-vendors/syncsort/
tableau software http://www.dbms2.com/category/products-and-vendors/tableau-software/
talend http://www.dbms2.com/category/products-and-vendors/talend/
teradata http://www.dbms2.com/category/products-and-vendors/teradata/
tokutek and tokudb http://www.dbms2.com/category/products-and-vendors/tokutek-tokudb/
truviso http://www.dbms2.com/category/products-and-vendors/truviso/
vectorwise http://www.dbms2.com/category/products-and-vendors/vectorwise/
vertica systems http://www.dbms2.com/category/products-and-vendors/vertica-systems/
voltdb and h-store http://www.dbms2.com/category/products-and-vendors/h-store/
wibidata http://www.dbms2.com/category/products-and-vendors/odiago-wibidata/
workday http://www.dbms2.com/category/products-and-vendors/workday-inc/
xkoto http://www.dbms2.com/category/products-and-vendors/xkoto-gridscale/
xtremedata http://www.dbms2.com/category/products-and-vendors/xtremedata-dbx/
yarcdata and cray http://www.dbms2.com/category/products-and-vendors/yarcdata-cray/
zettaset http://www.dbms2.com/category/products-and-vendors/zettaset/
zoomdata http://www.dbms2.com/category/products-and-vendors/zoomdata/
data integration and middleware http://www.dbms2.com/category/data-integration-middleware/
application servers http://www.dbms2.com/category/data-integration-middleware/application-servers/
eai, eii, etl, elt, etlt http://www.dbms2.com/category/data-integration-middleware/data-etl-etl-etlt-eai-eii/
data types http://www.dbms2.com/category/datatype/
gis and geospatial http://www.dbms2.com/category/datatype/gis-geographic-geospatial/
object http://www.dbms2.com/category/datatype/object-oriented-database-management/
rdf and graphs http://www.dbms2.com/category/datatype/rdf-graph-database/
structured documents http://www.dbms2.com/category/datatype/native-xml-database/
text http://www.dbms2.com/category/datatype/text-search/
dbms product categories http://www.dbms2.com/category/database-management-system/
archiving and information preservation http://www.dbms2.com/category/database-management-system/archiving-and-information-preservation/
data warehouse appliances http://www.dbms2.com/category/database-management-system/data-warehouse-appliances/
mid-range http://www.dbms2.com/category/database-management-system/mid-range/
newsql http://www.dbms2.com/category/database-management-system/newsql/
oltp http://www.dbms2.com/category/database-management-system/online-transaction-processing-oltp/
open source http://www.dbms2.com/category/database-management-system/open-source/
emulation, transparency, portability http://www.dbms2.com/category/database-emulation-transparency-portability-compatibility/
fun stuff http://www.dbms2.com/category/fun-stuff/
humor http://www.dbms2.com/category/fun-stuff/humor/
market share and customer counts http://www.dbms2.com/category/market-share/
memory-centric data management http://www.dbms2.com/category/memory-centric-data-management/
cache http://www.dbms2.com/category/memory-centric-data-management/cache/
in-memory dbms http://www.dbms2.com/category/memory-centric-data-management/in-memory-dbms/
streaming and complex event processing (cep) http://www.dbms2.com/category/memory-centric-data-management/event-stream-processing/
michael stonebraker http://www.dbms2.com/category/michael-stonebraker/
parallelization http://www.dbms2.com/category/parallelization/
clustering http://www.dbms2.com/category/parallelization/database-clustering/
mapreduce http://www.dbms2.com/category/parallelization/mapreduce/
transparent sharding http://www.dbms2.com/category/parallelization/transparent-sharding/
presentations http://www.dbms2.com/category/presentations/
pricing http://www.dbms2.com/category/pricing/
public policy http://www.dbms2.com/category/technology-public-policy/
surveillance and privacy http://www.dbms2.com/category/technology-public-policy/privacy-surveillance-liberty/
software as a service (saas) http://www.dbms2.com/category/software-as-a-service-database-saas/
cloud computing http://www.dbms2.com/category/software-as-a-service-database-saas/cloud-computing/
specific users http://www.dbms2.com/category/users/
ebay http://www.dbms2.com/category/users/ebay/
facebook http://www.dbms2.com/category/users/facebook-cassandra/
fox and myspace http://www.dbms2.com/category/users/fox-interactive-media-myspace/
jpmorgan chase http://www.dbms2.com/category/users/jpmorgan-chase/
teoco http://www.dbms2.com/category/users/teoco/
yahoo http://www.dbms2.com/category/users/yahoo/
zynga http://www.dbms2.com/category/users/zynga/
storage http://www.dbms2.com/category/storage/
solid-state memory http://www.dbms2.com/category/storage/solid-state-memory-disk-flash/
theory and architecture http://www.dbms2.com/category/database-theory-practice/
columnar database management http://www.dbms2.com/category/database-theory-practice/columnar-database-management/
data models and architecture http://www.dbms2.com/category/database-theory-practice/data-models/
data pipelining http://www.dbms2.com/category/database-theory-practice/data-pipelining/
database compression http://www.dbms2.com/category/database-theory-practice/database-compression/
database diversity http://www.dbms2.com/category/database-theory-practice/database-diversity/
derived data http://www.dbms2.com/category/database-theory-practice/derived-data/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
petabyte-scale data management http://www.dbms2.com/category/database-theory-practice/petabyte-database-theory-practice/
schema on need http://www.dbms2.com/category/database-theory-practice/schema-on-need/
sql/hadoop integration http://www.dbms2.com/category/database-theory-practice/sqlhadoop-integration/
workload management http://www.dbms2.com/category/database-theory-practice/workload-management/
transrelational http://www.dbms2.com/category/transrelational/
uncategorized http://www.dbms2.com/category/uncategorized/
monash research http://www.monash.com
white papers http://www.monash.com/whitepapers.html
log in http://www.dbms2.com/wp-login.php
home http://www.dbms2.com
about http://www.dbms2.com/database-blog/
contact http://www.monash.com/contact.html
feeds http://www.monash.com/signup.html
monash research http://www.monash.com
melissa bradshaw http://www.melissabradshaw.com

Zdjęcia

Zdjęcia 5
Zdjęcia bez atrybutu ALT 0
Zdjęcia bez atrybutu TITLE 5
Korzystanie Obraz ALT i TITLE atrybutu dla każdego obrazu.

Zdjęcia bez atrybutu TITLE

http://www.dbms2.com/wp-content/themes/monash/images/trans.gif
http://www.dbms2.com/wp-content/themes/monash/images/trans.gif
http://www.dbms2.com/wp-includes/images/smilies/icon_smile.gif
http://www.dbms2.com/wp-includes/images/smilies/icon_smile.gif
http://www.dbms2.com/wp-content/themes/monash/images/feed-icon-blue.gif

Zdjęcia bez atrybutu ALT

empty

Ranking:


Alexa Traffic
Daily Global Rank Trend
Daily Reach (Percent)









Majestic SEO











Text on page:

--> home about contact feeds november 23, 2016 dbas of the future after a july visit to datastax, i wrote the idea that nosql does away with dbas (database administrators) is common. it also turns out to be wrong. dbas basically do two things. handle the database design part of application development. in nosql environments, this part of the job is indeed largely refactored away. more precisely, it is integrated into the general app developer/architect role. manage production databases. this part of the dba job is, if anything, a bigger deal in the nosql world than in more mature and automated relational environments. it’s likely to be called part of “devops” rather than “dba”, but by whatever name it’s very much a thing. that turns out to understate the core point, which is that dbas still matter in non-rdbms environments. specifically, it’s too narrow in two ways. first, it’s generally too narrow as to what dbas do; people with dba-like skills are also involved in other areas such as “data governance”, “information lifecycle management”, storage, or what i like to call data mustering. second — and more narrowly — the first bullet point of the quote is actually incorrect. in fact, the database design part of application development can be done by a specialized person up front in the nosql world, just as it commonly is for rdbms apps. my wake-up call for that latter bit was a recent mongodb 3.4 briefing. mongodb certainly has various efforts in administrative tools, which i won’t recapitulate here. but to my surprise, mongodb also found a role for something resembling relational database design. the idea is simple: a database administrator defines a view against a mongodb database, where views: read more categories: databricks, spark and bdas, hadoop, mongodb, nosql, streaming and complex event processing (cep) leave a comment --> november 23, 2016 mongodb 3.4 and “multimodel” query “multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. my clients at mongodb of course had to join the train as well, but they’ve taken a clear and interesting stance: a query layer with multiple ways to query and analyze data. a separate data storage layer in which you have a choice of data storage engines … … each of which has the same logical (json-based) data structure. when i pointed out that it would make sense to call this “multimodel query” — because the storage isn’t “multimodel” at all — they quickly agreed. to be clear: while there are multiple ways to read data in mongodb, there’s still only one way to write it. letting that sink in helps clear up confusion as to what about mongodb is or isn’t “multimodel”. to spell that out a bit further: read more categories: database diversity, emulation, transparency, portability, mongodb, mysql, nosql, open source, rdf and graphs, structured documents, text leave a comment --> october 21, 2016 rapid analytics “real-time” technology excites people, and has for decades. yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. here are some notes on that conundrum. 1. i recently posted that “real-time” is getting real. but there are multiple technology challenges involved, including: general streaming. some of my posts on that subject are linked at the bottom of my august post on flink. low-latency ingest of data into structures from which it can be immediately analyzed. that helps drive the (re)integration of operational data stores, analytic data stores, and other analytic support — e.g. via spark. business intelligence that can be used quickly enough. this is a major ongoing challenge. my clients at zoomdata may be thinking about this area more clearly than most, but even they are still in the early stages of providing what users need. advanced analytics that can be done quickly enough. answers there may come through developments in anomaly management, but that area is still in its super-early days. alerting, which has been under-addressed for decades. perhaps the anomaly management vendors will finally solve it. 2. in early 2011, i coined the phrase investigative analytics, about which i said three main things: read more categories: business intelligence, databricks, spark and bdas, in-memory dbms, investment research and trading, log analysis, streaming and complex event processing (cep), text, web analytics, zoomdata 2 comments --> october 10, 2016 notes on anomaly management then felt i like some watcher of the skies when a new planet swims into his ken — john keats, “on first looking into chapman’s homer” 1. in june i wrote about why anomaly management is hard. well, not only is it hard to do; it’s hard to talk about as well. one reason, i think, is that it’s hard to define what an anomaly is. and that’s a structural problem, not just a semantic one — if something is well enough understood to be easily described, then how much of an anomaly is it after all? artificial intelligence is famously hard to define for similar reasons. “anomaly management” and similar terms are not yet in the software marketing mainstream, and may never be. but naming aside, the actual subject matter is important. 2. anomaly analysis is clearly at the heart of several sectors, including: it operations factory and other physical-plant operations security anti-fraud anti-terrorism each of those areas features one or both of the frameworks: surprises are likely to be bad. coincidences are likely to be suspicious. so if you want to identify, understand, avert and/or remediate bad stuff, data anomalies are the first place to look. 3. the “insights” promised by many analytics vendors — especially those who sell to marketing departments — are also often heralded by anomalies. already in the 1970s, walmart observed that red clothing sold particularly well in omaha, while orange flew off the shelves in syracuse. and so, in large college towns, they stocked their stores to the gills with clothing in the colors of the local football team. they also noticed that fancy dresses for little girls sold especially well in hispanic communities … specifically for girls at the age of first communion. read more categories: business intelligence, log analysis, predictive modeling and advanced analytics, web analytics leave a comment --> october 3, 2016 notes on the transition to the cloud 1. the cloud is super-hot. duh. and so, like any hot buzzword, “cloud” means different things to different marketers. four of the biggest things that have been called “cloud” are: the amazon cloud, microsoft azure, and their competitors, aka public cloud. software as a service, aka saas. co-location in off-premises data centers, aka colo. on-premises clusters (truly on-prem or colo as the case may be) designed to run a broad variety of applications, aka private cloud. further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. a number of vendors have backed away from such stories, but a few are still pushing it, including oracle and microsoft. this is a good example of monash’s laws of commercial semantics. 2. due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). the rest should use some combination of colo, saas, and public cloud. this fact now seems to be widely understood. read more categories: amazon and its cloud, cloud computing, data mart outsourcing, data warehousing, database diversity, google, hadoop, health care, investment research and trading, log analysis, microsoft and sql*server, nosql, software as a service (saas), solid-state memory, splunk, surveillance and privacy, text, web analytics 3 comments --> september 6, 2016 “real-time” is getting real i’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. some of those debates are by now pretty much settled. in particular: yes, interactive computer response is crucial. into the 1980s, many apps were batch-only. demand for such apps dried up. business intelligence should occur at interactive speeds, which is a major reason that there’s a market for high-performance analytic rdbms. theoretical arguments about “true” real-time vs. near-real-time are often pointless. what matters in most cases is human users’ perceptions of speed. most of the exceptions to that rule occur when machines race other machines, for example in automated bidding (high frequency trading or otherwise) or in network security. a big issue that does remain open is: how fresh does data need to be? my preferred summary answer is: as fresh as is needed to support the best decision-making. i think that formulation starts with several advantages: it respects the obvious point that different use cases require different levels of data freshness. it cautions against people who think they need fresh information but aren’t in a position to use it. (such users have driven much bogus “real-time” demand in the past.) it covers cases of both human and automated decision-making. straightforward applications of this principle include: read more categories: business intelligence, data warehousing, eai, eii, etl, elt, etlt, in-memory dbms, investment research and trading, log analysis, memsql, nosql, predictive modeling and advanced analytics, streaming and complex event processing (cep), web analytics 3 comments --> august 28, 2016 are analytic rdbms and data warehouse appliances obsolete? i used to spend most of my time — blogging and consulting alike — on data warehouse appliances and analytic dbms. now i’m barely involved with them. the most obvious reason is that there have been drastic changes in industry structure: many of the independent vendors were swooped up by acquisition. ibm bought netezza. microsoft bought datallegro. hp bought vertica. greenplum went to emc/vmware/pivotal. teradata bought aster. actian bought both paraccel and vectorwise. none of those acquisitions was a big success. microsoft did little with datallegro. netezza struggled with r&d after being bought by ibm. an ibmer recently told me that their main analytic rdbms engine was blu. i hear about vertica more as a technology to be replaced than as a significant ongoing market player. pivotal open-sourced greenplum. i have detected few people who care. ditto for actian’s offerings. teradata claimed a few large aster accounts, but i never hear of aster as something to compete or partner with. smaller vendors fizzled too. hadapt and kickfire went to teradata as more-or-less acquihires. infinidb folded. etc. impala and other hadoop-based alternatives are technology options. oracle, microsoft, ibm and to some extent sap/sybase are still pedaling along … but i rarely talk with companies that big. simply reciting all that, however, begs the question of whether one should still care about analytic rdbms at all. my answer, in a nutshell, is: analytic rdbms — whether on premises in software, in the form of data warehouse appliances, or in the cloud – are still great for hard-core business intelligence, where “hard-core” can refer to ad-hoc query complexity, reporting/dashboard concurrency, or both. but they aren’t good for much else. read more categories: actian and ingres, amazon and its cloud, aster data, business intelligence, calpont, cloud computing, data warehouse appliances, data warehousing, databricks, spark and bdas, datallegro, eai, eii, etl, elt, etlt, emc, exasol, greenplum, hadapt, hadoop, hp and neoview, ibm and db2, in-memory dbms, infobright, kickfire, kognitio, memsql, microsoft and sql*server, netezza, open source, oracle, paraccel, predictive modeling and advanced analytics, sap ag, sql/hadoop integration, sybase, teradata, vectorwise, vertica systems 27 comments --> august 21, 2016 introduction to data artisans and flink data artisans and flink basics start: flink is an apache project sponsored by the berlin-based company data artisans. flink has been viewed in a few different ways, all of which are similar to how spark is seen. in particular, per co-founder kostas tzoumas: flink’s original goal was “hadoop done right”. now flink is focused on streaming analytics, as an alternative to spark streaming, samza, et al. kostas seems to see flink as a batch-plus-streaming engine that’s streaming-first. like many open source projects, flink seems to have been partly inspired by a google paper. to this point, data artisans and flink have less maturity and traction than databricks and spark. for example:  read more categories: cloudera, databricks, spark and bdas, eai, eii, etl, elt, etlt, hadoop, hortonworks, intel, market share and customer counts, open source, streaming and complex event processing (cep) 3 comments --> august 21, 2016 more about databricks and spark databricks ceo ali ghodsi checked in because he disagreed with part of my recent post about databricks. ali’s take on databricks’ position in the spark world includes: what i called databricks’ “secondary business” of “licensing stuff to spark distributors” was really about second/third tier support. fair enough. but distributors of stacks including spark, for whatever combination of on-premise and cloud as the case may be, may in many cases be viewed as competitors to databricks cloud-only service. so why should databricks help them? databricks’ investment in spark summit and similar evangelism is larger than i realized. ali suggests that the fraction of databricks’ engineering devoted to open source spark is greater than i understood during my recent visit. ali also walked me through customer use cases and adoption in wonderful detail. in general: a large majority of databricks customers have machine learning use cases. predicting and preventing user/customer churn is a huge issue across multiple market sectors. the story on those sectors, per ali, is:  read more categories: data warehousing, databricks, spark and bdas, investment research and trading, market share and customer counts, open source, predictive modeling and advanced analytics, software as a service (saas), specific users, streaming and complex event processing (cep), web analytics 3 comments --> august 7, 2016 notes on datastax and cassandra i visited datastax on my recent trip. that was a tipping point leading to my recent discussions of nosql dbas and misplaced fear of vendor lock-in. but of course i also learned some things about datastax and cassandra themselves. on the customer side: datastax customers still overwhelmingly use cassandra for internet back-ends — web, mobile or otherwise as the case might be. this includes — and “includes” might be understating the point — traditional enterprises worried about competition from internet-only ventures. customers in large numbers want cloud capabilities, as a potential future if not a current need. one customer example was a large retailer, who in the past was awful at providing accurate inventory information online, but now uses cassandra for that. datastax brags that its queries come back in 20 milliseconds, but that strikes me as a bit beside the point; what really matters is that data accuracy has gone from “batch” to some version of real-time. also, microsoft is a datastax customer, using cassandra (and spark) for the office 365 backend, or at least for the associated analytics. per patrick mcfadin, the four biggest things in datastax enterprise 5 are: read more categories: cassandra, datastax, microsoft and sql*server, nosql, specific users 2 comments --> next page → subscribe to the monash research feed via rss or email: login search our blogs and white papers --> monash research blogs dbms 2 covers database management, analytics, and related technologies. text technologies covers text mining, search, and social software. strategic messaging analyzes marketing and messaging strategy. the monash report examines technology and public policy issues. software memories recounts the history of the software industry. user consulting building a short list? refining your strategic plan? we can help. vendor advisory we tell vendors what's happening -- and, more important, what they should do about it. recent white paper the explosion in dbms choice august, 2008 recent webcast what leading database vendors don't want you to know originally broadcast april 9, 2008 --> monash research highlights learn about white papers, webcasts, and blog highlights, by rss or email. recent posts dbas of the future mongodb 3.4 and “multimodel” query rapid analytics notes on anomaly management notes on the transition to the cloud categories about this blog analytic glossary analytic technologies business intelligence data mart outsourcing data warehousing molap predictive modeling and advanced analytics application areas games and virtual worlds health care investment research and trading log analysis scientific research telecommunications web analytics buying processes benchmarks and pocs companies and products 1010data ab initio software actian and ingres aerospike akiban aleri and coral8 algebraix alpha five amazon and its cloud ants software aster data ayasdi basho and riak business objects calpont cassandra cast iron systems cirro citus data clearstory data cloudant cloudera clustrix cogito and 7 degrees cognos continuent couchbase couchdb databricks, spark and bdas datallegro datameer datastax dataupia dbshards and codefutures elastra emc endeca enterprisedb and postgres plus exasol expressor filemaker geniedb gooddata google greenplum groovy corporation hadapt hadoop hbase hortonworks hp and neoview ibm and db2 purexml illuminate solutions infobright informatica information builders inforsense intel intersystems and cache' jaspersoft kafka and confluent kalido kaminario kickfire kognitio kxen mapr marklogic mcobject memcached memsql metamarkets and druid microsoft and sql*server microstrategy monetdb mongodb mysql neo technology and neo4j netezza nuodb nutonian objectivity and infinite graph oracle exadata oracle timesten paraccel pentaho pervasive software pivotlink platfora postgresql progress, apama, and datadirect qliktech and qlikview rainstor revolution analytics rocana salesforce.com sand technology sap ag sas institute scalebase scaledb schooner information technology scidb sensage snaplogic software ag soliddb splunk starcounter streambase sybase syncsort tableau software talend teradata tokutek and tokudb truviso vectorwise vertica systems voltdb and h-store wibidata workday xkoto xtremedata yarcdata and cray zettaset zoomdata data integration and middleware application servers eai, eii, etl, elt, etlt data types gis and geospatial object rdf and graphs structured documents text dbms product categories archiving and information preservation data warehouse appliances mid-range newsql oltp open source emulation, transparency, portability fun stuff humor market share and customer counts memory-centric data management cache in-memory dbms streaming and complex event processing (cep) michael stonebraker parallelization clustering mapreduce transparent sharding presentations pricing public policy surveillance and privacy software as a service (saas) cloud computing specific users ebay facebook fox and myspace jpmorgan chase teoco yahoo zynga storage solid-state memory theory and architecture columnar database management data models and architecture data pipelining database compression database diversity derived data nosql petabyte-scale data management schema on need sql/hadoop integration workload management transrelational uncategorized date archives select month november 2016 october 2016 september 2016 august 2016 july 2016 june 2016 may 2016 february 2016 january 2016 december 2015 november 2015 october 2015 september 2015 august 2015 july 2015 june 2015 may 2015 april 2015 march 2015 february 2015 january 2015 december 2014 november 2014 october 2014 september 2014 august 2014 july 2014 june 2014 may 2014 april 2014 march 2014 february 2014 january 2014 december 2013 november 2013 october 2013 september 2013 august 2013 july 2013 june 2013 may 2013 april 2013 march 2013 february 2013 january 2013 december 2012 november 2012 october 2012 september 2012 august 2012 july 2012 june 2012 may 2012 april 2012 march 2012 february 2012 january 2012 november 2011 october 2011 september 2011 august 2011 july 2011 june 2011 may 2011 april 2011 march 2011 february 2011 january 2011 december 2010 november 2010 october 2010 september 2010 august 2010 july 2010 june 2010 may 2010 april 2010 march 2010 february 2010 january 2010 december 2009 november 2009 october 2009 september 2009 august 2009 july 2009 june 2009 may 2009 april 2009 march 2009 february 2009 january 2009 december 2008 november 2008 october 2008 september 2008 august 2008 july 2008 june 2008 may 2008 april 2008 march 2008 february 2008 january 2008 december 2007 november 2007 october 2007 september 2007 august 2007 july 2007 june 2007 may 2007 april 2007 march 2007 february 2007 january 2007 december 2006 november 2006 october 2006 september 2006 august 2006 july 2006 june 2006 may 2006 april 2006 march 2006 february 2006 january 2006 december 2005 november 2005 october 2005 september 2005 august 2005 links monash research white papers admin log in home about contact feeds copyright © monash research, 2005-2008. theme designed by melissa bradshaw.


Here you find all texts from your page as Google (googlebot) and others search engines seen it.

Words density analysis:

Numbers of all words: 3314

One word

Two words phrases

Three words phrases

and - 3.86% (128)
the - 3.53% (117)
data - 3.2% (106)
are - 2.02% (67)
for - 1.12% (37)
that - 1.09% (36)
all - 1.03% (34)
analytic - 0.91% (30)
log - 0.88% (29)
use - 0.81% (27)
cloud - 0.75% (25)
out - 0.75% (25)
age - 0.72% (24)
per - 0.63% (21)
analytics - 0.6% (20)
2016 - 0.57% (19)
about - 0.57% (19)
august - 0.54% (18)
more - 0.54% (18)
but - 0.54% (18)
databricks - 0.51% (17)
may - 0.51% (17)
app - 0.51% (17)
spark - 0.51% (17)
our - 0.48% (16)
2008 - 0.45% (15)
dbms - 0.45% (15)
database - 0.45% (15)
october - 0.45% (15)
— - 0.45% (15)
software - 0.42% (14)
manage - 0.42% (14)
november - 0.42% (14)
red - 0.42% (14)
one - 0.42% (14)
his - 0.42% (14)
real - 0.42% (14)
what - 0.42% (14)
management - 0.39% (13)
search - 0.39% (13)
--> - 0.39% (13)
had - 0.39% (13)
september - 0.39% (13)
2007 - 0.36% (12)
july - 0.36% (12)
2011 - 0.36% (12)
2010 - 0.36% (12)
2012 - 0.36% (12)
2013 - 0.36% (12)
2014 - 0.36% (12)
mongodb - 0.36% (12)
2015 - 0.36% (12)
with - 0.36% (12)
june - 0.36% (12)
categories - 0.36% (12)
2009 - 0.36% (12)
not - 0.36% (12)
read - 0.36% (12)
ali - 0.36% (12)
part - 0.36% (12)
2006 - 0.36% (12)
research - 0.33% (11)
some - 0.33% (11)
here - 0.33% (11)
market - 0.33% (11)
customer - 0.33% (11)
nosql - 0.33% (11)
intel - 0.33% (11)
technology - 0.33% (11)
streaming - 0.33% (11)
time - 0.33% (11)
april - 0.33% (11)
this - 0.33% (11)
january - 0.33% (11)
which - 0.33% (11)
february - 0.33% (11)
microsoft - 0.3% (10)
march - 0.3% (10)
vendor - 0.3% (10)
have - 0.3% (10)
dba - 0.3% (10)
business - 0.3% (10)
recent - 0.3% (10)
categories: - 0.3% (10)
form - 0.3% (10)
case - 0.3% (10)
datastax - 0.3% (10)
call - 0.3% (10)
tell - 0.3% (10)
flink - 0.3% (10)
december - 0.3% (10)
comment - 0.3% (10)
web - 0.27% (9)
anomaly - 0.27% (9)
intelligence - 0.27% (9)
they - 0.27% (9)
open - 0.27% (9)
hadoop - 0.27% (9)
still - 0.27% (9)
hard - 0.27% (9)
like - 0.27% (9)
way - 0.27% (9)
point - 0.27% (9)
real-time - 0.27% (9)
analytics, - 0.24% (8)
can - 0.24% (8)
even - 0.24% (8)
any - 0.24% (8)
user - 0.24% (8)
source - 0.24% (8)
was - 0.24% (8)
sand - 0.24% (8)
also - 0.24% (8)
vendors - 0.21% (7)
there - 0.21% (7)
post - 0.21% (7)
only - 0.21% (7)
cases - 0.21% (7)
view - 0.21% (7)
other - 0.21% (7)
event - 0.21% (7)
rdbms - 0.21% (7)
has - 0.21% (7)
monash - 0.21% (7)
too - 0.21% (7)
comments - 0.21% (7)
cassandra - 0.21% (7)
complex - 0.21% (7)
it’s - 0.21% (7)
large - 0.21% (7)
than - 0.21% (7)
dbas - 0.21% (7)
memory - 0.21% (7)
been - 0.21% (7)
information - 0.18% (6)
text - 0.18% (6)
bdas - 0.18% (6)
2005 - 0.18% (6)
application - 0.18% (6)
need - 0.18% (6)
“real-time” - 0.18% (6)
notes - 0.18% (6)
should - 0.18% (6)
well - 0.18% (6)
first - 0.18% (6)
trading - 0.18% (6)
big - 0.18% (6)
clear - 0.18% (6)
now - 0.18% (6)
analysis - 0.18% (6)
things - 0.18% (6)
investment - 0.18% (6)
query - 0.18% (6)
(cep) - 0.18% (6)
processing - 0.18% (6)
“multimodel - 0.18% (6)
appliances - 0.18% (6)
databricks, - 0.18% (6)
advanced - 0.18% (6)
users - 0.18% (6)
using - 0.18% (6)
bought - 0.18% (6)
ibm - 0.18% (6)
similar - 0.15% (5)
different - 0.15% (5)
public - 0.15% (5)
many - 0.15% (5)
intelligence, - 0.15% (5)
aka - 0.15% (5)
who - 0.15% (5)
area - 0.15% (5)
few - 0.15% (5)
main - 0.15% (5)
predictive - 0.15% (5)
their - 0.15% (5)
its - 0.15% (5)
systems - 0.15% (5)
modeling - 0.15% (5)
oracle - 0.15% (5)
early - 0.15% (5)
see - 0.15% (5)
those - 0.15% (5)
it. - 0.15% (5)
“multimodel” - 0.15% (5)
nosql, - 0.15% (5)
bdas, - 0.15% (5)
service - 0.15% (5)
ways - 0.15% (5)
storage - 0.15% (5)
care - 0.15% (5)
aster - 0.15% (5)
paper - 0.15% (5)
blog - 0.15% (5)
warehousing - 0.15% (5)
teradata - 0.15% (5)
warehouse - 0.15% (5)
much - 0.15% (5)
design - 0.15% (5)
most - 0.15% (5)
into - 0.15% (5)
is, - 0.15% (5)
counts - 0.15% (5)
specific - 0.15% (5)
neo - 0.12% (4)
integration - 0.12% (4)
sql*server - 0.12% (4)
object - 0.12% (4)
white - 0.12% (4)
cast - 0.12% (4)
back - 0.12% (4)
iron - 0.12% (4)
datallegro - 0.12% (4)
amazon - 0.12% (4)
emc - 0.12% (4)
both - 0.12% (4)
etlt - 0.12% (4)
warehousing, - 0.12% (4)
cloud, - 0.12% (4)
etl, - 0.12% (4)
fact - 0.12% (4)
reason - 0.12% (4)
is: - 0.12% (4)
fresh - 0.12% (4)
example - 0.12% (4)
think - 0.12% (4)
including - 0.12% (4)
eai, - 0.12% (4)
eii, - 0.12% (4)
elt, - 0.12% (4)
help - 0.12% (4)
greenplum - 0.12% (4)
actian - 0.12% (4)
run - 0.12% (4)
netezza - 0.12% (4)
engine - 0.12% (4)
colo - 0.12% (4)
vertica - 0.12% (4)
artisans - 0.12% (4)
on-prem - 0.12% (4)
databricks’ - 0.12% (4)
al. - 0.12% (4)
off - 0.12% (4)
admin - 0.12% (4)
world - 0.12% (4)
in-memory - 0.12% (4)
trading, - 0.12% (4)
analysis, - 0.12% (4)
second - 0.12% (4)
such - 0.12% (4)
multiple - 0.12% (4)
people - 0.12% (4)
matter - 0.12% (4)
you - 0.12% (4)
hadoop, - 0.12% (4)
enough - 0.12% (4)
… - 0.12% (4)
how - 0.12% (4)
source, - 0.12% (4)
future - 0.12% (4)
general - 0.12% (4)
from - 0.12% (4)
answer - 0.09% (3)
less - 0.09% (3)
share - 0.09% (3)
counts, - 0.09% (3)
new - 0.09% (3)
especially - 0.09% (3)
human - 0.09% (3)
stuff - 0.09% (3)
hot - 0.09% (3)
customers - 0.09% (3)
machine - 0.09% (3)
story - 0.09% (3)
google - 0.09% (3)
layer - 0.09% (3)
there’s - 0.09% (3)
rapid - 0.09% (3)
covers - 0.09% (3)
analyze - 0.09% (3)
21, - 0.09% (3)
sap - 0.09% (3)
premises - 0.09% (3)
kickfire - 0.09% (3)
hadapt - 0.09% (3)
etlt, - 0.09% (3)
when - 0.09% (3)
hear - 0.09% (3)
paraccel - 0.09% (3)
quickly - 0.09% (3)
leave - 0.09% (3)
papers - 0.09% (3)
mongodb, - 0.09% (3)
computing - 0.09% (3)
vectorwise - 0.09% (3)
relational - 0.09% (3)
automated - 0.09% (3)
product - 0.09% (3)
cache - 0.09% (3)
(saas) - 0.09% (3)
diversity - 0.09% (3)
graph - 0.09% (3)
two - 0.09% (3)
away - 0.09% (3)
does - 0.09% (3)
idea - 0.09% (3)
visit - 0.09% (3)
after - 0.09% (3)
sybase - 0.09% (3)
likely - 0.09% (3)
includes - 0.09% (3)
bit - 0.09% (3)
enterprise - 0.09% (3)
feed - 0.09% (3)
something - 0.09% (3)
decision-making. - 0.09% (3)
3.4 - 0.09% (3)
technologies - 0.09% (3)
done - 0.09% (3)
called - 0.09% (3)
development - 0.09% (3)
learn - 0.09% (3)
areas - 0.09% (3)
involved - 0.09% (3)
narrow - 0.09% (3)
core - 0.09% (3)
memsql - 0.09% (3)
issue - 0.09% (3)
actual - 0.09% (3)
through - 0.09% (3)
companies - 0.09% (3)
zoomdata - 0.09% (3)
major - 0.09% (3)
home - 0.09% (3)
so, - 0.09% (3)
understood - 0.09% (3)
stores - 0.09% (3)
sql*server, - 0.09% (3)
place - 0.09% (3)
enough. - 0.09% (3)
ken - 0.09% (3)
support - 0.09% (3)
mart - 0.09% (3)
seems - 0.09% (3)
used - 0.09% (3)
marketing - 0.09% (3)
good - 0.09% (3)
define - 0.09% (3)
cloud. - 0.09% (3)
dbms, - 0.09% (3)
apps - 0.09% (3)
(cep), - 0.09% (3)
want - 0.09% (3)
why - 0.06% (2)
centers, - 0.06% (2)
outsourcing - 0.06% (2)
rss - 0.06% (2)
role - 0.06% (2)
ingres - 0.06% (2)
calpont - 0.06% (2)
talk - 0.06% (2)
management, - 0.06% (2)
do; - 0.06% (2)
administrator - 0.06% (2)
cloudera - 0.06% (2)
transition - 0.06% (2)
“cloud” - 0.06% (2)
policy - 0.06% (2)
then - 0.06% (2)
highlights - 0.06% (2)
blogs - 0.06% (2)
report - 0.06% (2)
four - 0.06% (2)
search, - 0.06% (2)
webcast - 0.06% (2)
point, - 0.06% (2)
biggest - 0.06% (2)
are: - 0.06% (2)
and, - 0.06% (2)
just - 0.06% (2)
strategic - 0.06% (2)
text, - 0.06% (2)
messaging - 0.06% (2)
found - 0.06% (2)
occur - 0.06% (2)
postgres - 0.06% (2)
never - 0.06% (2)
often - 0.06% (2)
privacy - 0.06% (2)
management” - 0.06% (2)
architecture - 0.06% (2)
anomalies - 0.06% (2)
job - 0.06% (2)
bad - 0.06% (2)
turns - 0.06% (2)
be. - 0.06% (2)
portability - 0.06% (2)
wrote - 0.06% (2)
datastax, - 0.06% (2)
security - 0.06% (2)
operations - 0.06% (2)
sectors, - 0.06% (2)
23, - 0.06% (2)
feeds - 0.06% (2)
contact - 0.06% (2)
clothing - 0.06% (2)
sold - 0.06% (2)
plus - 0.06% (2)
kognitio - 0.06% (2)
exasol - 0.06% (2)
that’s - 0.06% (2)
hbase - 0.06% (2)
hortonworks - 0.06% (2)
against - 0.06% (2)
db2 - 0.06% (2)
infobright - 0.06% (2)
whatever - 0.06% (2)
mapr - 0.06% (2)
documents - 0.06% (2)
semantic - 0.06% (2)
specifically - 0.06% (2)
mysql - 0.06% (2)
girls - 0.06% (2)
little - 0.06% (2)
environments. - 0.06% (2)
splunk - 0.06% (2)
mature - 0.06% (2)
graphs - 0.06% (2)
neoview - 0.06% (2)
leading - 0.06% (2)
where - 0.06% (2)
industry - 0.06% (2)
(saas), - 0.06% (2)
stores, - 0.06% (2)
memsql, - 0.06% (2)
transparency, - 0.06% (2)
emulation, - 0.06% (2)
diversity, - 0.06% (2)
consulting - 0.06% (2)
dbms. - 0.06% (2)
helps - 0.06% (2)
structured - 0.06% (2)
datallegro. - 0.06% (2)
while - 0.06% (2)
went - 0.06% (2)
health - 0.06% (2)
isn’t - 0.06% (2)
because - 0.06% (2)
sense - 0.06% (2)
make - 0.06% (2)
rdf - 0.06% (2)
solid-state - 0.06% (2)
computing, - 0.06% (2)
remain - 0.06% (2)
posts - 0.06% (2)
including: - 0.06% (2)
matters - 0.06% (2)
getting - 0.06% (2)
machines - 0.06% (2)
recently - 0.06% (2)
demand - 0.06% (2)
were - 0.06% (2)
interactive - 0.06% (2)
applications - 0.06% (2)
yet - 0.06% (2)
debates - 0.06% (2)
decades. - 0.06% (2)
obvious - 0.06% (2)
require - 0.06% (2)
drive - 0.06% (2)
aren’t - 0.06% (2)
position - 0.06% (2)
surveillance - 0.06% (2)
pivotal - 0.06% (2)
via - 0.06% (2)
past - 0.06% (2)
stacks - 0.06% (2)
least - 0.06% (2)
take - 0.06% (2)
clearly - 0.06% (2)
really - 0.06% (2)
distributors - 0.06% (2)
on-premise - 0.06% (2)
competitors - 0.06% (2)
number - 0.06% (2)
providing - 0.06% (2)
clients - 0.06% (2)
need. - 0.06% (2)
private - 0.06% (2)
broad - 0.06% (2)
come - 0.06% (2)
subject - 0.06% (2)
internet - 0.06% (2)
otherwise - 0.06% (2)
might - 0.06% (2)
designed - 0.06% (2)
ongoing - 0.06% (2)
course - 0.06% (2)
oracle, - 0.06% (2)
data, - 0.06% (2)
several - 0.06% (2)
each - 0.06% (2)
spark. - 0.06% (2)
combination - 0.06% (2)
appliances, - 0.06% (2)
great - 0.06% (2)
hard-core - 0.06% (2)
refer - 0.06% (2)
sql/hadoop - 0.06% (2)
well, - 0.06% (2)
choice - 0.06% (2)
rest - 0.06% (2)
project - 0.06% (2)
viewed - 0.06% (2)
true - 0.06% (2)
kostas - 0.06% (2)
original - 0.06% (2)
alternative - 0.06% (2)
own - 0.06% (2)
whether - 0.06% (2)
in the - 0.39% (13)
of the - 0.36% (12)
more categories: - 0.3% (10)
read more - 0.3% (10)
at the - 0.27% (9)
business intelligence - 0.24% (8)
comments --> - 0.21% (7)
open source - 0.21% (7)
web analytics - 0.18% (6)
streaming and - 0.18% (6)
spark and - 0.18% (6)
and bdas - 0.18% (6)
databricks, spark - 0.18% (6)
complex event - 0.18% (6)
processing (cep) - 0.18% (6)
to the - 0.18% (6)
part of - 0.18% (6)
of data - 0.18% (6)
notes on - 0.18% (6)
anomaly management - 0.18% (6)
event processing - 0.18% (6)
and complex - 0.18% (6)
monash research - 0.15% (5)
warehouse appliances - 0.15% (5)
investment research - 0.15% (5)
modeling and - 0.15% (5)
analytic rdbms - 0.15% (5)
research and - 0.15% (5)
data warehouse - 0.15% (5)
and advanced - 0.15% (5)
predictive modeling - 0.15% (5)
software as - 0.15% (5)
which i - 0.15% (5)
business intelligence, - 0.15% (5)
data artisans - 0.12% (4)
are still - 0.12% (4)
my recent - 0.12% (4)
hard to - 0.12% (4)
eii, etl, - 0.12% (4)
3 comments - 0.12% (4)
advanced analytics, - 0.12% (4)
and trading, - 0.12% (4)
the cloud - 0.12% (4)
eai, eii, - 0.12% (4)
log analysis, - 0.12% (4)
can be - 0.12% (4)
microsoft and - 0.12% (4)
data warehousing, - 0.12% (4)
etl, elt, - 0.12% (4)
is that - 0.12% (4)
as the - 0.12% (4)
a service - 0.12% (4)
--> august - 0.12% (4)
open source, - 0.12% (4)
on the - 0.09% (3)
than i - 0.09% (3)
and flink - 0.09% (3)
use cases - 0.09% (3)
elt, etlt, - 0.09% (3)
in-memory dbms, - 0.09% (3)
2016 notes - 0.09% (3)
to what - 0.09% (3)
the case - 0.09% (3)
share and - 0.09% (3)
analytics 3 - 0.09% (3)
and other - 0.09% (3)
and its - 0.09% (3)
categories: data - 0.09% (3)
leave a - 0.09% (3)
21, 2016 - 0.09% (3)
--> october - 0.09% (3)
a comment - 0.09% (3)
categories: business - 0.09% (3)
mongodb 3.4 - 0.09% (3)
comment --> - 0.09% (3)
ibm and - 0.09% (3)
here are - 0.09% (3)
market share - 0.09% (3)
likely to - 0.09% (3)
and customer - 0.09% (3)
database design - 0.09% (3)
service (saas) - 0.09% (3)
the idea - 0.09% (3)
cloud computing - 0.09% (3)
specific users - 0.09% (3)
trading, log - 0.09% (3)
on that - 0.09% (3)
processing (cep), - 0.09% (3)
flink is - 0.06% (2)
datastax and - 0.06% (2)
but they - 0.06% (2)
structured documents - 0.06% (2)
emulation, transparency, - 0.06% (2)
i also - 0.06% (2)
of vendor - 0.06% (2)
warehouse appliances, - 0.06% (2)
whether on - 0.06% (2)
and to - 0.06% (2)
data management - 0.06% (2)
people who - 0.06% (2)
or otherwise - 0.06% (2)
went to - 0.06% (2)
me through - 0.06% (2)
a large - 0.06% (2)
service (saas), - 0.06% (2)
and architecture - 0.06% (2)
sql/hadoop integration - 0.06% (2)
home about - 0.06% (2)
and graphs - 0.06% (2)
recent post - 0.06% (2)
combination of - 0.06% (2)
public policy - 0.06% (2)
cassandra for - 0.06% (2)
to some - 0.06% (2)
to spark - 0.06% (2)
2 comments - 0.06% (2)
a google - 0.06% (2)
databricks and - 0.06% (2)
the monash - 0.06% (2)
technology and - 0.06% (2)
vertica systems - 0.06% (2)
what they - 0.06% (2)
spark is - 0.06% (2)
might be - 0.06% (2)
--> monash - 0.06% (2)
customer counts, - 0.06% (2)
and “multimodel” - 0.06% (2)
warehousing, databricks, - 0.06% (2)
cloud computing, - 0.06% (2)
actian and - 0.06% (2)
data mart - 0.06% (2)
and neoview - 0.06% (2)
hp and - 0.06% (2)
about contact - 0.06% (2)
and data - 0.06% (2)
are multiple - 0.06% (2)
of course - 0.06% (2)
a clear - 0.06% (2)
ways to - 0.06% (2)
data storage - 0.06% (2)
of which - 0.06% (2)
to call - 0.06% (2)
rapid analytics - 0.06% (2)
at least - 0.06% (2)
“real-time” technology - 0.06% (2)
for decades. - 0.06% (2)
technology to - 0.06% (2)
“real-time” is - 0.06% (2)
some of - 0.06% (2)
data stores, - 0.06% (2)
clients at - 0.06% (2)
my clients - 0.06% (2)
management is - 0.06% (2)
but that - 0.06% (2)
the nosql - 0.06% (2)
dbas of - 0.06% (2)
the future - 0.06% (2)
after a - 0.06% (2)
i wrote - 0.06% (2)
turns out - 0.06% (2)
into the - 0.06% (2)
too narrow - 0.06% (2)
“multimodel” query - 0.06% (2)
what i - 0.06% (2)
— and - 0.06% (2)
— the - 0.06% (2)
application development - 0.06% (2)
for that - 0.06% (2)
november 23, - 0.06% (2)
3.4 and - 0.06% (2)
about this - 0.06% (2)
still in - 0.06% (2)
(cep), web - 0.06% (2)
database diversity, - 0.06% (2)
in which - 0.06% (2)
this is - 0.06% (2)
centers, aka - 0.06% (2)
public cloud. - 0.06% (2)
23, 2016 - 0.06% (2)
computing, data - 0.06% (2)
sql*server, nosql, - 0.06% (2)
data centers, - 0.06% (2)
surveillance and - 0.06% (2)
getting real - 0.06% (2)
which is - 0.06% (2)
a major - 0.06% (2)
there’s a - 0.06% (2)
most of - 0.06% (2)
intelligence, data - 0.06% (2)
case may - 0.06% (2)
biggest things - 0.06% (2)
which has - 0.06% (2)
the actual - 0.06% (2)
dbms, investment - 0.06% (2)
on anomaly - 0.06% (2)
only is - 0.06% (2)
it’s hard - 0.06% (2)
that it’s - 0.06% (2)
an anomaly - 0.06% (2)
and similar - 0.06% (2)
each of - 0.06% (2)
transition to - 0.06% (2)
or both - 0.06% (2)
are likely - 0.06% (2)
the first - 0.06% (2)
are also - 0.06% (2)
and so, - 0.06% (2)
in large - 0.06% (2)
well in - 0.06% (2)
contact feeds - 0.06% (2)
read more categories: - 0.3% (10)
event processing (cep) - 0.18% (6)
and complex event - 0.18% (6)
streaming and complex - 0.18% (6)
databricks, spark and - 0.18% (6)
complex event processing - 0.18% (6)
data warehouse appliances - 0.15% (5)
investment research and - 0.15% (5)
predictive modeling and - 0.15% (5)
modeling and advanced - 0.15% (5)
spark and bdas, - 0.15% (5)
eii, etl, elt, - 0.12% (4)
as a service - 0.12% (4)
software as a - 0.12% (4)
and advanced analytics, - 0.12% (4)
comments --> august - 0.12% (4)
microsoft and sql*server - 0.12% (4)
research and trading, - 0.12% (4)
likely to be - 0.09% (3)
and trading, log - 0.09% (3)
data artisans and - 0.09% (3)
more categories: business - 0.09% (3)
web analytics 3 - 0.09% (3)
artisans and flink - 0.09% (3)
leave a comment - 0.09% (3)
event processing (cep), - 0.09% (3)
share and customer - 0.09% (3)
and customer counts - 0.09% (3)
analytics 3 comments - 0.09% (3)
a comment --> - 0.09% (3)
etl, elt, etlt, - 0.09% (3)
microsoft and sql*server, - 0.09% (3)
of the future - 0.06% (2)
and customer counts, - 0.06% (2)
counts, open source, - 0.06% (2)
datastax and cassandra - 0.06% (2)
2 comments --> - 0.06% (2)
--> monash research - 0.06% (2)
dbas of the - 0.06% (2)
and “multimodel” query - 0.06% (2)
august 21, 2016 - 0.06% (2)
in-memory dbms, investment - 0.06% (2)
data warehousing, databricks, - 0.06% (2)
it’s hard to - 0.06% (2)
part of application - 0.06% (2)
november 23, 2016 - 0.06% (2)
mongodb 3.4 and - 0.06% (2)
there are multiple - 0.06% (2)
is a major - 0.06% (2)
can be done - 0.06% (2)
dbms, investment research - 0.06% (2)
on anomaly management - 0.06% (2)
notes on the - 0.06% (2)
data warehouse appliances, - 0.06% (2)
transition to the - 0.06% (2)
data centers, aka - 0.06% (2)
this is a - 0.06% (2)
and its cloud, - 0.06% (2)
cloud computing, data - 0.06% (2)
business intelligence, data - 0.06% (2)
the database design - 0.06% (2)
on data warehouse - 0.06% (2)
home about contact - 0.06% (2)

Here you can find chart of all your popular one, two and three word phrases. Google and others search engines means your page is about words you use frequently.

Copyright © 2015-2016 hupso.pl. All rights reserved. FB | +G | Twitter

Hupso.pl jest serwisem internetowym, w którym jednym kliknieciem możesz szybko i łatwo sprawdź stronę www pod kątem SEO. Oferujemy darmowe pozycjonowanie stron internetowych oraz wycena domen i stron internetowych. Prowadzimy ranking polskich stron internetowych oraz ranking stron alexa.