Big Data Analytics for Apache Impala
Impala, the SQL analytic engine shipped with Cloudera Enterprise, is a fully integrated, state-of-the-art analytic database architected specifically to leverage the flexibility and scalability of Apache Hadoop, which may contain many types of information and content including click stream, web and call center logs, and ID scans. Although most closely associated with Cloudera, Impala also ships with other Hadoop distributions including MapR, Oracle, and Amazon.
What is Apache Impala?
Apache Impala is an open-source, high-performance, distributed SQL query engine designed for fast, interactive analysis of data stored in Hadoop-based systems. Unlike other Hadoop querying methods that operate in batch-processing modes, Impala provides real-time query capabilities, making it an excellent choice for ad-hoc queries on large datasets. It allows users to execute SQL queries directly on Hadoop data stored in HDFS (Hadoop Distributed File System) and Apache HBase without requiring data movement or transformation, significantly speeding up data analysis tasks.
Impala supports the majority of SQL standards, enabling it to integrate seamlessly with existing BI tools and SQL-based applications. This compatibility allows analysts and data scientists to use familiar syntax and techniques to explore, analyze, and visualize data stored in Hadoop ecosystems.
Key Features of Apache Impala:
- Real-Time Query Performance: Impala is designed to offer low-latency and high-concurrency for SQL queries over large datasets, making it suitable for interactive applications and dashboards.
- Massively Parallel Processing (MPP): Utilizes MPP architecture to distribute queries across all nodes in the Hadoop cluster, allowing for scalable and efficient processing of large volumes of data.
- Broad SQL Support: Provides support for a wide range of SQL functionality, including JOINs, aggregations, and subqueries, facilitating complex data analysis and reporting tasks.
- Integration with Hadoop Ecosystem: Designed to work seamlessly within the Hadoop ecosystem, Impala can query data stored in HDFS, Apache HBase, and Apache Kudu, providing flexibility in how data is managed and analyzed.
- Security and Access Control: Supports Hadoop’s security features, including Kerberos authentication and role-based access control via Apache Sentry, ensuring data is protected and access is appropriately managed.
Use Cases for Apache Impala:
- Interactive Data Exploration: Analysts can quickly execute SQL queries to explore large datasets stored in Hadoop, gaining insights and identifying trends without significant delays.
- Business Intelligence (BI) and Reporting: With its SQL support, Impala integrates with BI tools, enabling the creation of reports and dashboards that reflect the current state of the data in Hadoop.
- Data Science and Advanced Analytics: Provides a SQL interface for data scientists to prepare and explore data before applying more complex analytical models, serving as a step in the broader data analysis workflow.
Why Apache Impala for Big Data Analytics?
The Impala platform brings scalable parallel database technology to Hadoop, enabling users to issue low-latency SQL queries to big data stored in HDFS and Apache HBase without requiring data movement or transformation.
With Impala came the Parquet columnar data storage format, which stores data more efficiently than row-based formats in HDFS. Although writing Parquet files means you need to determine the schema (tables, columns) in advance and write the data in a specific way, the upside is much faster analysis.
Impala enables analysts and data scientists to perform real-time, interactive analytics on data stored in Hadoop via SQL or business intelligence tools.
Logi Composer and Apache Impala
Logi Composer was one of the first certified Impala big data analytics and visualization software tools, and the results of this collaboration have been dramatic. While legacy BI tools use JDBC or ODBC to query Impala as if it were a relational database, Logi Composer connects to Impala via native APIs and understands the Parquet partitioning scheme.
It uses this information to break up single logical queries into multiple micro-queries. Micro-queries submitted to Impala return at different points in time. Logi Composer displays a preliminary visualization as soon as the first micro-query returns and then sharpens the visualization as additional micro-queries complete. The result: much faster response time, analysis, and insights.
Logi Composer: The Embedding Experience
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