Drill is an Apache open-source SQL query engine for Big Data exploration. Drill is designed from the ground up to support high-performance analysis on the semi-structured and rapidly evolving data coming from modern Big Data applications, while still providing the familiarity and ecosystem of ANSI SQL, the industry-standard query language. Drill provides plug-and-play integration with existing Apache Hive and Apache HBase deployments.
Top 10 Reasons to Use Drill
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- Drill is the world's first and only distributed SQL engine that doesn't require schemas. It shares the same schema-free JSON model as MongoDB and Elasticsearch.
- you can query complex, semi-structured data in situ
- Drill supports the standard SQL:2003 syntax. No need to learn a new "SQL-like" language or struggle with a semi-functional BI tool.
- You can use your existing BI (Business Intelligence) tools, such as Tableau, MicroStrategy, QlikView and Excel
- You can join tables associated with different Hive metastores, and you can join a Hive table with an HBase table or a directory of log files
- Drill is extensible. You can connect Drill out-of-the-box to file systems (local or distributed, such as S3 and HDFS), HBase and Hive.
- Drill exposes a simple, high-performance Java API to build custom user-defined functions (UDFs) for adding your own business logic to Drill
- Drill is designed from the ground up for high throughput and low latency. It doesn't use a general purpose execution engine like MapReduce, Tez or Spark.
- Drill is available as a simple download you can run on your laptop. When you're ready to analyze larger datasets, deploy Drill on your Hadoop cluster (up to 1000 commodity servers)
For Intergrating Apache Drill on your cluster environment, drop an email at firstname.lastname@example.org
Drill supports a variety of NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. A single query can join data from multiple datastores. For example, you can join a user profile collection in MongoDB with a directory of event logs in Hadoop.