Test the latest features with SQL Server 2017 Developer Edition

Are you curious how new capabilities in SQL Server will impact your applications? Download and test SQL Server 2017 Developer Edition, a full-featured free edition, licensed for use as a development and test database in a non-production environment. You can browse and access SQL Server training code samples at GitHub, and when you’re ready, check out your options to move to production.

With SQL Server 2017, you can build modern applications using the language of your choice, on-premises or in the cloud, on Windows, Linux, and Docker containers. Your mission-critical programs will benefit from industry-leading scalability, performance, and availability. And SQL Server 2017 is the only commercial database with AI built-in, enabling you to build intelligent applications using scalable and highly parallelized R and Python. Read the datasheet and white paper for more details.

If you’re a Linux developer, SQL Server 2017 brings the database you want to the platform you love—and don’t just take our word for it. Browse technical FAQs from your peers in our quick start guide.

SQL Server 2017 Developer Edition does not include a licensed OS, such as a license for Windows 10 included on a new laptop. 90 to 180 day free trials of Windows and Windows Server are available on the TechNet Evaluation Center.

If you are intersting to learn SQL server training go through enrolling by database administrator course

Want to stay ahead of the game? Find out about the SQL Server 2019 Community Technology Preview (CTP) in our free on-demand webinar and sign up for the Early Adoption Program.

SQL Server 2019 makes it easier to manage a big data environment and provides key elements of a data lake—Hadoop Distributed File System (HDFS), Apache SparkTM, and analytics tools—deeply integrated with SQL Server and fully supported by Microsoft. Easily deploy using Linux containers on a Kubernetes-managed cluster. Read the white paper to learn more.

Features of Spark SQL

Let’s take a stroll into the aspects which make Spark SQL so popular in data processing.

Integrated – One can mix SQL queries with Spark programs easily. Structured data can be queried inside Spark programs using Spark SQL using either SQL or a Data frame API. Running SQL queries alongside analytic algorithms is easy because of this tight integration.

Hive compatibility – Hive queries can be run as it is as Spark SQL supports HiveQL along with UDFs (user defined functions) and Hive SerDes. This allows one to access the existing Hive warehouses.

Unified data access – Loading and querying data from variety of sources is possible. One only needs a single interface to work with structured data which the schema-RDDs provide.

Standard connectivity – Spark SQL includes a server mode with high grade connectivity to JDBC or ODBC.

Performance and scalability – To make queries agile alongside computing hundreds of nodes using the Spark engine, Spark SQL incorporates a code generator, cost-based optimizer and columnar storage. This provides complete mid-query fault tolerance. Note that we discusses earlier in Hive limitations that this kind of tolerance was lacking in Hive. Spark has ample information regarding the structure of the data as well as the type of computation being performed which is provided by the interfaces of Spark SQL. This leads to extra optimization from Spark SQL internally. Faster execution of Hive queries is possible as Spark SQL can directly read from multiple sources like HDFS, Hive, and existing RDDs etc.

Use cases

There is a lot to learn about Spark SQL as how it is applied in industry scenario but the below three use cases can give an apt idea:

Twitter sentiment analysis – Initially all data is got from Spark streaming. Later Spark SQL is used to analyse everything about a topic say Narendra Modi. Every tweet regarding Modi is got and then Spark SQL does its magic to classify tweets as neutral tweets, positive tweets, negative tweets, very positive tweets and very negative tweets. This is just one of the ways how sentiment analysis is done. This is useful in target marketing, crisis management and service adjusting.

Stock market analysis – Once you are streaming data in the real time you can also do the processing in the real time. Stock movements, market movement generate so much data and traders need an edge, an analytics framework which will calculate all the data in real time and provide the most rewarding stock or contract all within the nick of time. As said earlier if there is a need for real time analytics framework then Spark and its components is the technology to be considered.

Banking – Real time processing is required in credit card fraud detection. Assume a transaction happens in Bangalore where there is a purchase of 4,000 rupees swiping a credit card. Within 5 minutes there is another purchase of 10,000 rupees in Kolkata swiping the same credit card. Banks can make use of real time analytics provided by Spark SQL in detecting the fraud.

Narayana was a python developer form 2015 she was the member of core python developer of the company she is enthusiastic about python.