September 2014
Intermediate to advanced
152 pages
4h 3m
English
Content preview from Getting Started with Impala
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Chapter 1. Why Impala?
The Apache Hadoop ecosystem is very data-centric, making it a natural fit for database developers with SQL experience. Much application development work for Hadoop consists of writing programs to copy, convert or reorganize, and analyze data files. A lot of effort goes into finding ways to do these things reliably, on a large scale, and in parallel across clusters of networked machines. Impala focuses on making these activities fast and easy, without requiring you to have a PhD in distributed computing, learn a lot of new APIs, or write a complete program when your intent can be conveyed with a single SQL statement.
Impala’s Place in the Big Data Ecosystem
The Cloudera Impala project arrives in the Big Data world at just the right moment. Data volume is growing fast, outstripping what can be realistically stored or processed on a single server. The Hadoop software stack is opening that field up to a larger audience of users and developers.
Impala brings a high degree of flexibility to the familiar database ETL process. You can query data that you already have in various standard Hadoop file formats (see “File Formats”). You can access the same data with a combination of Impala and other Hadoop components such as Apache Hive, Apache Pig, and Cloudera Search without duplicating or converting the data. When query speed is critical, the Parquet columnar file format makes it simple to reorganize data for maximum performance of data warehouse-style queries.
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