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Apache Drill’s Self-Service Capabilities By @MapR | @CloudExpo [#BigData]

Big Data is a jungle: rich with resources, abundant in growth, but also a bit overwhelming and easy to get lost in

Help Yourself: Leveraging Apache Drill's Self-Service Capabilities

Small data management solutions don't work in our brave new Big Data world. Back in the small data days, we talked proudly about having gigabytes of structured data that had been carefully denormalized to reduce latency as much as possible. Today's data is measured in petabytes, and it is dynamic, complex, and wildly varied in structure.

Small data was a nicely planned garden, but Big Data is a jungle: rich with resources, abundant in growth, but also a bit overwhelming and easy to get lost in.

Exploring that jungle requires solutions that enable interactive, self-service ways to work with historical as well as near real-time data. Hadoop and NoSQL on Hadoop solved a significant amount of Big Data access and availability problems. Add Apache Drill and SQL-on-Hadoop to the mix and you have a solution designed to enable easy analysis of complex data structures and datasets using the well-known SQL semantics.

If you want to blaze a path through the Big Data jungle, you want Apache Drill in your solution set.

Dig Deep with Drill
Apache Drill is a SQL query engine that works with numerous underlying data formats and sources. As a standalone query engine that supports multiple data sources, it works with the Hadoop and NoSQL database solutions that an organization may already have in place.

Apache Drill excels in demanding situations that require low latency performance, such as data exploration, data discovery, ad hoc business intelligence (BI) queries, and Day Zero analytics. It enables efficient analytics operations ranging from a fast overview of a specific dataset to an extended, explorative analysis of a very large data pool. Apache Drill supports interactive queries, rather than batch-oriented requests. It scales from a single laptop to a large cluster of servers easily.

And it's user-friendly. With minimal IT involvement, Apache Drill enables data to be queried in its native formats, including nested data, schema-less data and dynamic data. There is no need to explicitly define and maintain schemas; Drill can automatically leverage the structure embedded in the data. This enables self-service data exploration. Live data can be worked with upon its arrival with no need to prepare a schema and massage the data into a query-ready form. Analysts can change data sources on the fly without getting hung up waiting for DBA services to structure that newly requested data.

Analysts can also leverage their existing SQL skills and BI tools to directly query self-describing data and process complex data types. This closes the hole that had existed between the standard SQL and Big Data solutions built for efficient use of Big Data, such as Hadoop-based systems, and the need for SQL compatibility to access structured databases.

While we may quietly pride ourselves on the glorious Bigness of our Big Data, we all know that the data in itself is of little value. It's the knowledge that can be gained from it that is priceless. Apache Drill is an essential tool to knock down the wall that had kept businesses from fully harnessing the power of Big Data.

Wait, what about...?
You may be wondering why such a fuss is being made in business and technology circles right now about Apache Drill. After all, there are dozens of other proprietary and open source projects providing SQL or SQL-comparable features on Hadoop.

The problem is that many of these solutions were designed with a "backwardly compatible" mindset. The intent was to take technology designed for small data and engineer it to work in a Big Data world. Useful tools were developed, but it's now time to develop solutions that are designed specifically to support the new ways that we use data.

While Apache Drill was initially inspired by Google's Dremel project, it is now a vehicle that can be used to bring forward-looking technologies to Big Data. Apache Drill is the ideal interactive SQL engine for Hadoop, which rapidly continues to gain popularity, as Apache Drill fully supports Hadoop's (and HBase's) flexibility and agility. Apache Drill is the only SQL engine for Hadoop that doesn't demand schemas to be created and maintained or data to be transformed before it can be queried.

Validated and Approved
The open source community has greatly refined the original features of Google's Dremel, with enhanced capabilities including the extensibility of its architecture, overall agility, support for full SQL, optional schema handling, and its ability to handle nested data (such as JSON, Protobuf, Parquet).

The Apache Software Foundation announced in December 2014 that it has promoted Drill to a top-level project at Apache, where it joins other illustrious projects such as Apache Hadoop and httpd (the world's most popular Web server).

Drill's promotion to a top-level project demonstrates that Drill has a strong community of users and developers. Users can be confident that the project has proven itself and has a viable roadmap for its development. The community will continue to advance Apache Drill's key technologies and performance.

It's time to stop looking to the past for answers and begin driving the future. If you're ready to test-drive Drill, you can do so using the MapR Sandbox for Hadoop, which runs on PC, Mac and Linux platforms. MapR Technologies is the provider of the top-ranked distribution for Apache Hadoop.

You can also view a tutorial on analyzing real-world data using Drill.

More Stories By Nitin Bandugula

As a Sr. Product Marketing Manager at MapR, Nitin brings his engineering, business and management skills together to market technology products. At MapR, Nitin focuses on SQL, batch and in-memory frameworks and streaming technologies on Hadoop. Prior to MapR, Nitin worked for enterprise companies and startups in various roles including Engineering, Product Management and Management Consulting. Nitin holds a Masters degree in Computer Science from the Illinois Institute of Technology and an MBA from the Johnson School at Cornell University.

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