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Apache Drill vs Apache Impala
May 25, 2023 | Author: Michael Stromann
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Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage. Get faster insights without the overhead (data loading, schema creation and maintenance, transformations, etc.). Analyze the multi-structured and nested data in non-relational datastores directly without transforming or restricting the data
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Apache Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.
Apache Drill and Apache Impala are both open-source query engines designed for fast and interactive analytics on large-scale data sets, but they have some key differences in their architecture and features.
1. Architecture: Apache Drill is designed to work with a variety of data sources, including structured, semi-structured, and unstructured data, utilizing a schema-free approach. It leverages a distributed execution engine to process queries across multiple nodes. On the other hand, Apache Impala focuses primarily on structured data stored in Apache Hadoop, using a distributed in-memory architecture that provides high-performance query processing.
2. SQL Compatibility: Both Apache Drill and Apache Impala support ANSI SQL, but there are some differences in their SQL dialects and capabilities. Apache Drill aims for maximum SQL compatibility, supporting a wide range of SQL functions, data types, and query syntax, including support for nested data structures and schema-less data. Apache Impala also supports ANSI SQL, but it focuses more on traditional SQL constructs and performance optimizations for structured data processing.
3. Data Source Support: Apache Drill has a broader scope when it comes to data source support. It can query a wide variety of data sources, including relational databases, Hadoop Distributed File System (HDFS), NoSQL databases, cloud storage, and more. Apache Impala, on the other hand, is primarily focused on querying data stored in Hadoop, including HDFS, Apache HBase, and Apache Kudu.
4. Maturity and Adoption: Apache Impala has been around for a longer time and has gained significant adoption, especially within the Hadoop ecosystem. It is widely used in organizations that heavily rely on Hadoop for their data processing needs. Apache Drill is relatively newer and may have a smaller user base, but it has gained popularity for its ability to query diverse data sources and handle complex data structures.
See also: Top 10 Big Data platforms
1. Architecture: Apache Drill is designed to work with a variety of data sources, including structured, semi-structured, and unstructured data, utilizing a schema-free approach. It leverages a distributed execution engine to process queries across multiple nodes. On the other hand, Apache Impala focuses primarily on structured data stored in Apache Hadoop, using a distributed in-memory architecture that provides high-performance query processing.
2. SQL Compatibility: Both Apache Drill and Apache Impala support ANSI SQL, but there are some differences in their SQL dialects and capabilities. Apache Drill aims for maximum SQL compatibility, supporting a wide range of SQL functions, data types, and query syntax, including support for nested data structures and schema-less data. Apache Impala also supports ANSI SQL, but it focuses more on traditional SQL constructs and performance optimizations for structured data processing.
3. Data Source Support: Apache Drill has a broader scope when it comes to data source support. It can query a wide variety of data sources, including relational databases, Hadoop Distributed File System (HDFS), NoSQL databases, cloud storage, and more. Apache Impala, on the other hand, is primarily focused on querying data stored in Hadoop, including HDFS, Apache HBase, and Apache Kudu.
4. Maturity and Adoption: Apache Impala has been around for a longer time and has gained significant adoption, especially within the Hadoop ecosystem. It is widely used in organizations that heavily rely on Hadoop for their data processing needs. Apache Drill is relatively newer and may have a smaller user base, but it has gained popularity for its ability to query diverse data sources and handle complex data structures.
See also: Top 10 Big Data platforms
