Apache Impala is a way to carry out SQL queries on Apache Hadoop files. The rapid query response of Apache Impala makes exploring interactive, in contrast to the long batch jobs with SQL-on-Hadoop veterans.
You get human-scale response times with Apache Impala, with applications including:
- Querying new data types, even when no ETL’s have been created for them yet
- Enabling HDFS final destination data querying
- Enabling self-tool generation reporting
- Providing constant monitoring on incoming data
Unlike Hive, whose only real application nowadays is for ETLs and batch-processing, Apache Impala gets you your answers rapidly and interactively. Of course, Apache Impala lacks the fault-tolerance of Hive- but that’s where Apache Spark streaming comes in.
Apache Spark Consulting
Apache Spark utilizes in-memory processing tools to make interactive analysis of huge datasets even faster and easier than with Apache Impala. How fast? With the proper consulting you can learn how to use Apache Spark to perform queries a 100-fold faster than with traditional tools like Hadoop.
Since Apache Spark is an open source Apache platform, it is also extremely cost effective, providing a cost-effective way to conduct real-time analytics and business intelligence. Apache Spark consulting can be leveraged by your organization to generate and integrate massive distributed data sets in single-line encoding, access extensive HDFS, HBase, Cassandra and S3 databases, and easily optimize high level operators.
Apache Spark Streaming
Apache Spark Streaming makes constructing scalable fault tolerant streaming applications easy. It utilizes Apache Spark’s API in the stream processing arena, which makes it possible to code streaming jobs the exact same way batch jobs are coded.
Since it runs on Apache Spark, Spark streaming can recycle the same code for batch processing, compare historical data to streams, or perform ad-hoc queries. In other words, Apache Spark streaming does more than analyze data, it enables you to create robust interactive applications. It is also possible to recover, with no additional code, lost work and its associated operator state.