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U-SQL: A new language for Big Data.
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Big data has been the watchword for a while in IT, so it’s no surprise that data science skills are in hot demand and the job has perhaps mischievously
Of course, there is strong demand for data science specialists with NoSQL and Apache Hadoop knowledge, there has been a growth spurt for those well verses in RDBMSes, but NoSQL is still way behind SQL and that is a skill that was specifically named on more than half of the listings that CrowdFlower Inc analysed on LinkedIn. So if you are looking to beef up your skills, look no further than SQL.
This idea is not ‘new’ – one can say that federated systems is an old topic. But in fact, in a very heterogeneous environment where businesses (groups) are investing in different solutions, such as Hadoop, relational DW, document-oriented stores (e.g. MongoDB) & legacy OLTP systems – each optimized for different kinds of applications -, such federated capabilities become important. The ability of querying all of your company’s data, independent of where it resides, what format it is stored in, in a performing way is crucial in today’s data-centric world with massive, increasing data volume.
Artin Avanes, Solution Architect at Microsoft - 2015
Vendors such as Cloudera, Databricks Inc. (the commercial entity behind Spark), Hortonworks, and MapR, along with IBM Corp. and Teradata, have invested significantly in shoring up Hadoop's ANSI SQL bona fides. Cloudera via its investments in Impala, an interactive SQL-like query engine for Hadoop; Hortonworks via its work with Hive (a SQL interpreter for Hadoop) and Tez (a replacement for Hadoop's MapReduce engine that supports interactive processing); MapR via its work with Drill, the open source implementation of Google Inc.'s Dremel distributed query technology; Databricks and IBM via their investments in Spark (which has its own SQL variant, Spark SQL); and Teradata via its investment in Presto, a SQL query engine for Hadoop. If the data warehouse as an institution is dying, data warehouse architecture -- as a conceptual framework -- is alive and well.
Stephen Swoyer, november 2015
Faster Processing, Faster Insight: How to Use Machine Learning with Spark SQL Data, Tableau Analytics & Simba ODBC Connectivity. For more information visit: ...
Nice webinar.
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At the present time the continuous availability of data is very important. The downtime of a few seconds can generate a huge loss in business and a company's reputation.
This article cleary ignores the existence of modern SQL databases like HP Vertica, but it has great images and explains NoSQL very well.
The continuous availability of data section was my favorite.
At the present time the continuous availability of data is very important. The downtime of a few seconds can generate a huge loss in business and a company's reputation. The best solution to avoid this is to use a distributed approach. NoSQL also works on a distributed approach. In a distributed approach we remove dependency from a single machine and spread it out on several machines. If one or more database servers or "nodes" go down then the other nodes in the system are able to continue with operations without data loss. NoSQL databases work on a distributed approach so a NoSQL database is able to provide continuous availability whether in single locations, across data centers and in the cloud.
While NoSQL use is growing very rapidly, why isn’t it everywhere yet? Why isn’t NoSQL already the standard database for web, mobile, and IoT applications? Why are people still force-fitting JSON into relational databases? The simple answer is because NoSQL lacks a comprehensive and powerful query language
Timothy Stephan, product marketing leader at Couchbase - August 2015
For more information, click here.
