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

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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
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H1
introduction to crate.io and cratedb
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
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
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- 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
introduction to crate.io and cratedb http://www.dbms2.com/2016/12/18/introduction-to-crate-io-and-cratedb/
analytic rdbms vendors http://www.dbms2.com/2016/08/28/are-analytic-rdbms-and-data-warehouse-appliances-obsolete/
read more http://www.dbms2.com/2016/12/18/introduction-to-crate-io-and-cratedb/#more-10149
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/
databricks, spark and bdas http://www.dbms2.com/category/products-and-vendors/databricks-bdas-spark-shark-berkeley-amplab/
gis and geospatial http://www.dbms2.com/category/datatype/gis-geographic-geospatial/
memsql http://www.dbms2.com/category/products-and-vendors/memsql/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
open source http://www.dbms2.com/category/database-management-system/open-source/
structured documents http://www.dbms2.com/category/datatype/native-xml-database/
2 comments http://www.dbms2.com/2016/12/18/introduction-to-crate-io-and-cratedb/#comments
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/
2 comments http://www.dbms2.com/2016/11/23/mongodb-3-4-and-multimodel-query/#comments
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/
4 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/
28 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
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
introduction to crate.io and cratedb http://www.dbms2.com/2016/12/18/introduction-to-crate-io-and-cratedb/
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/
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/
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--> home about contact feeds december 18, 2016 introduction to crate.io and cratedb crate.io and cratedb basics include: crate.io makes cratedb. cratedb is a quasi-rdbms designed to receive sensor data and similar iot (internet of things) inputs. cratedb’s creators were perhaps a little slow to realize that the “r” part was needed, but are playing catch-up in that regard. crate.io is an outfit founded by austrian guys, headquartered in berlin, that is turning into a san francisco company. crate.io says it has 22 employees and 5 paying customers. crate.io cites bigger numbers than that for confirmed production users, clearly active clusters, and overall product downloads. in essence, cratedb is an open source and less mature alternative to memsql. the opportunity for memsql and cratedb alike exists in part because analytic rdbms vendors didn’t close it off. cratedb’s not-just-relational story starts: a column can contain ordinary values (of usual-suspect datatypes) or “objects”, … … where “objects” presumably are the kind of nested/hierarchical structures that are common in the nosql/internet-backend world, … … except when they’re just blobs (binary large objects). there’s a way to manually define “strict schemas” on the structured objects, and a syntax for navigating their structure in where clauses. there’s also a way to automagically infer “dynamic schemas”, but it’s simplistic enough to be more suitable for development/prototyping than for serious production. read more categories: columnar database management, data models and architecture, databricks, spark and bdas, gis and geospatial, memsql, nosql, open source, structured documents 2 comments --> 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 2 comments --> 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 4 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 28 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 --> 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 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