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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.

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Keywords dbms, database, business intelligence, bi, data warehouse, relational database management system
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one bit of news in trump’s speech
coordination, the underused “c” word
there’s no escape from politics now
politics and policy in the age of trump
introduction to crate.io and cratedb
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cloudera’s data science workbench http://www.dbms2.com/2017/03/19/cloudera-data-science-workbench/
read more http://www.dbms2.com/2017/03/19/cloudera-data-science-workbench/#more-10215
cloudera http://www.dbms2.com/category/products-and-vendors/cloudera/
hadoop http://www.dbms2.com/category/products-and-vendors/hadoop-products-and-vendors/
market share and customer counts http://www.dbms2.com/category/market-share/
predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
1 comment http://www.dbms2.com/2017/03/19/cloudera-data-science-workbench/#comments
introduction to sequoiadb and sequoiacm http://www.dbms2.com/2017/03/12/introduction-to-sequoiadb-and-sequoiacm/
read more http://www.dbms2.com/2017/03/12/introduction-to-sequoiadb-and-sequoiacm/#more-10207
application areas http://www.dbms2.com/category/applications/
business intelligence http://www.dbms2.com/category/analytics-technologies/business-intelligence/
data models and architecture http://www.dbms2.com/category/database-theory-practice/data-models/
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/
market share and customer counts http://www.dbms2.com/category/market-share/
nosql http://www.dbms2.com/category/database-theory-practice/nosql/
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/
postgresql http://www.dbms2.com/category/products-and-vendors/postgresql/
sequoiadb http://www.dbms2.com/category/products-and-vendors/sequoiadb-sequoiacm/
structured documents http://www.dbms2.com/category/datatype/native-xml-database/
4 comments http://www.dbms2.com/2017/03/12/introduction-to-sequoiadb-and-sequoiacm/#comments
one bit of news in trump’s speech http://www.dbms2.com/2017/03/01/one-bit-of-news-in-trumps-speech/
transcript https://www.nytimes.com/2017/02/28/us/politics/trump-congress-video-transcript.html
sober https://www.washingtonpost.com/news/the-fix/wp/2017/02/28/winners-and-losers-from-president-trumps-big-speech-to-congress/
bannon opposes them http://www.dbms2.com/2017/02/02/theres-no-escape-from-politics/
negative-seeming news about h1-b visas http://www.sfchronicle.com/business/article/us-suspends-expedited-processing-for-h1-b-visas-10976085.php
public policy http://www.dbms2.com/category/technology-public-policy/
leave a comment http://www.dbms2.com/2017/03/01/one-bit-of-news-in-trumps-speech/#respond
coordination, the underused “c” word http://www.dbms2.com/2017/02/28/coordination-the-underused-c-word/
read more http://www.dbms2.com/2017/02/28/coordination-the-underused-c-word/#more-10194
business intelligence http://www.dbms2.com/category/analytics-technologies/business-intelligence/
predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
public policy http://www.dbms2.com/category/technology-public-policy/
2 comments http://www.dbms2.com/2017/02/28/coordination-the-underused-c-word/#comments
there’s no escape from politics now http://www.dbms2.com/2017/02/02/theres-no-escape-from-politics/
legal frameworks http://www.dbms2.com/2010/07/04/fair-data-use/
information use http://www.dbms2.com/2016/05/18/surveillance-data-in-ordinary-law-enforcement/
read more http://www.dbms2.com/2017/02/02/theres-no-escape-from-politics/#more-10161
public policy http://www.dbms2.com/category/technology-public-policy/
surveillance and privacy http://www.dbms2.com/category/technology-public-policy/privacy-surveillance-liberty/
5 comments http://www.dbms2.com/2017/02/02/theres-no-escape-from-politics/#comments
politics and policy in the age of trump http://www.dbms2.com/2017/02/02/politics-and-policy-in-the-age-of-trump/
read more http://www.dbms2.com/2017/02/02/politics-and-policy-in-the-age-of-trump/#more-10160
about this blog http://www.dbms2.com/category/housekeeping/
public policy http://www.dbms2.com/category/technology-public-policy/
surveillance and privacy http://www.dbms2.com/category/technology-public-policy/privacy-surveillance-liberty/
9 comments http://www.dbms2.com/2017/02/02/politics-and-policy-in-the-age-of-trump/#comments
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/
3 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/
3 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/
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one bit of news in trump’s speech http://www.dbms2.com/2017/03/01/one-bit-of-news-in-trumps-speech/
coordination, the underused “c” word http://www.dbms2.com/2017/02/28/coordination-the-underused-c-word/
there’s no escape from politics now http://www.dbms2.com/2017/02/02/theres-no-escape-from-politics/
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predictive modeling and advanced analytics http://www.dbms2.com/category/analytics-technologies/predictive-modeling-advanced-analytics/
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benchmarks and pocs http://www.dbms2.com/category/buying-processes/benchmarks-and-pocs/
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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/
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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/
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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/
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--> home about contact feeds march 19, 2017 cloudera’s data science workbench 0. matt brandwein of cloudera briefed me on the new cloudera data science workbench. the problem it purports to solve is: one way to do data science is to repeatedly jump through the hoops of working with a properly-secured hadoop cluster. this is difficult. another way is to extract data from a hadoop cluster onto your personal machine. this is insecure (once the data arrives) and not very parallelized. a third way is needed. cloudera’s idea for a third way is: you don’t run anything on your desktop/laptop machine except a browser. the browser connects you to a docker container that holds (and isolates) a kind of virtual desktop for you. the docker container runs on your cloudera cluster, so connectivity-to-hadoop and security are handled rather automagically. in theory, that’s pure goodness … assuming that the automagic works sufficiently well. i gather that cloudera data science workbench has been beta tested by 5 large organizations and many 10s of users. we’ll see what is or isn’t missing as more customers take it for a spin. read more categories: cloudera, hadoop, market share and customer counts, predictive modeling and advanced analytics 1 comment --> march 12, 2017 introduction to sequoiadb and sequoiacm for starters, let me say: sequoiadb, the company, is my client. sequoiadb, the product, is the main product of sequoiadb, the company. sequoiadb, the company, has another product line sequoiacm, which subsumes sequoiadb in content management use cases. sequoiadb, the product, is fundamentally a json data store. but it has a relational front end … … and is usually sold for rdbms-like use cases … … except when it is sold as part of sequoiacm, which adds in a large object/block store and a content-management-oriented library. sequoiadb’s products are open source. sequoiadb’s largest installation seems to be 2 pb across 100 nodes; that includes block storage. figures for dbms-only database sizes aren’t as clear, but the sweet spot of the cluster-size range for such use cases seems to be 6-30 nodes. also: sequoiadb, the company, was founded in toronto, by former ibm db2 folks. even so, it’s fairly accurate to view sequoiadb as a chinese company. specifically: sequoiadb’s founders were chinese nationals. most of them went back to china. other employees to date have been entirely chinese. sales to date have been entirely in china, but sequoiadb has international aspirations sequoiadb has >100 employees, a large majority of which are split fairly evenly between “engineering” and “implementation and technical support”. sequoiadb’s marketing (as opposed to sales) department is astonishingly tiny. sequoiadb cites >100 subscription customers, including 10 in the global fortune 500, a large fraction of which are in the banking sector. (other sectors mentioned repeatedly are government and telecom.) unfortunately, sequoiadb has not captured a lot of detailed information about unpaid open source production usage. read more categories: application areas, business intelligence, data models and architecture, data warehousing, databricks, spark and bdas, market share and customer counts, nosql, oltp, open source, postgresql, sequoiadb, structured documents 4 comments --> march 1, 2017 one bit of news in trump’s speech donald trump addressed congress tonight. as may be seen by the transcript, his speech — while uncharacteristically sober — was largely vacuous. that said, while steve bannon is firmly established as trump’s puppet master, they don’t agree on quite everything, and one of the documented disagreements had been in their view of skilled, entrepreneurial founder-type immigrants: bannon opposes them, but trump has disagreed with his view. and as per the speech, trump seems to be maintaining his disagreement. at least, that seems implied by his call for “a merit-based immigration system.” and by the way — trump managed to give a whole speech without saying anything overtly racist. indeed, he specifically decried the murder of an indian-immigrant engineer. by trump standards, that counts as a kind of progress. edit (march 5): but now there is negative-seeming news about h1-b visas. categories: public policy leave a comment --> february 28, 2017 coordination, the underused “c” word i’d like to argue that a single frame can be used to view a lot of the issues that we think about. specifically, i’m referring to coordination, which i think is a clearer way of characterizing much of what we commonly call communication or collaboration. it’s easy to argue that computing, to an overwhelming extent, is really about communication. most obviously: data is constantly moving around — across wide area networks, across local networks, within individual boxes, or even within particular chips. many major developments are almost purely about communication. the most important computing device today may be a telephone. the world wide web is essentially a publishing platform. social media are huge. etc. indeed, it’s reasonable to claim: when technology creates new information, it’s either analytics or just raw measurement. everything else is just moving information around, and that’s communication. a little less obvious is the much of this communication could be alternatively described as coordination. some communication has pure consumer value, such as when we talk/email/facebook/snapchat/facetime with loved ones. but much of the rest is for the purpose of coordinating business or technical processes. among the technical categories that boil down to coordination are: operating systems. anything to do with distributed computing. anything to do with system or cluster management. anything that’s called “collaboration”. that’s a lot of the value in “platform” it right there.  read more categories: business intelligence, predictive modeling and advanced analytics, public policy 2 comments --> february 2, 2017 there’s no escape from politics now the united states and consequently much of the world are in political uproar. much of that is about very general and vital issues such as war, peace or the treatment of women. but quite a lot of it is to some extent tech-industry-specific. the purpose of this post is outline how and why that is. for example: there’s a worldwide backlash against “elites” — and tech industry folks are perceived as members of those elites. that perception contains a lot of truth, and not just in terms of culture/education/geography. indeed, it may even be a bit understated, because trends commonly blamed on “trade” or “globalization” often have their roots in technological advances. there’s a worldwide trend towards authoritarianism. surveillance/ privacy and censorship issues are strongly relevant to that trend. social media companies are up to their neck in political considerations. because they involve grave threats to liberty, i see surveillance/privacy as the biggest technology-specific policy issues in the united states. (in other countries, technology-driven censorship might loom larger yet.) my views on privacy and surveillance have long been: fixing the legal frameworks around information use is a difficult and necessary job. the tech community should be helping more than it is. until those legal frameworks are indeed cleaned up, the only responsible alternative is to foot-drag on data collection, on data retention, and on the provision of data to governmental agencies. given the recent election of a us president with strong authoritarian tendencies, that foot-dragging is much more important than it was before. other important areas of technology/policy overlap include: read more categories: public policy, surveillance and privacy 5 comments --> february 2, 2017 politics and policy in the age of trump the united states presidency was recently assumed by an orwellian lunatic.* sadly, this is not an exaggeration. the dangers — both of authoritarianism and of general mis-governance — are massive. everybody needs in some way to respond. *”orwellian lunatic” is by no means an oxymoron. indeed, many of the most successful tyrants in modern history have been delusional; notable examples include hitler, stalin, mao and, more recently, erdogan. (by way of contrast, i view most other soviet/russian leaders and most jumped-up-colonel coup leaders as having been basically sane.) there are many candidates for what to focus on, including: technology-specific issues — e.g. privacy/surveillance, network neutrality, etc. issues in which technology plays a large role — e.g. economic changes that affect many people’s employment possibilities. subjects that may not be tech-specific, but are certainly of great importance. the list of candidates here is almost endless, such as health care, denigration of women, maltreatment of immigrants, or the possible breakdown of the whole international order. but please don’t just go on with your life and leave the politics to others. those “others” you’d like to rely on haven’t been doing a very good job. what i’ve chosen to do personally includes: read more categories: about this blog, public policy, surveillance and privacy 9 comments --> 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 3 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 3 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 --> 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 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Here you find all texts from your page as Google (googlebot) and others search engines seen it.

Words density analysis:

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