UPDATED 06:00 EDT / MARCH 28 2017

BIG DATA

JethroData cranks up speed and automation in business intelligence acceleration engine

JethroData Inc., provider of an index-based SQL platform that speeds up business intelligence operations on Hadoop as well as traditional data warehousing platforms, is today releasing a new version with improved security, automatic aggregation of decision-support cubes, an improved administrator interface and better integration with Apache Hive.

The company, which launched its platform last year after nearly four years in development, describes its namesake software as an acceleration layer that uses a combination of indexes and predefined “cubes” to turbocharge performance of BI queries.

Unlike dedicated business intelligence servers, Jethro can work directly on data sets in a Hadoop cluster, as well as more traditional data warehousing back ends like those from Teradata Corp. and IBM Corp.’s Netezza. The company says it solves the capacity problems that afflict dedicated BI servers. “Today’s data sets of 5 billion rows are too big for BI servers,” said CEO Eli Singer. “Instead of bringing data into memory, we push the queries out to the data source.”

Indexing is a way to speed database queries by using pointers to indicate records with similar characteristics. For example, an index might point to every occurrence of “60609” in a ZIP code column or “female” in a gender column. Indexes are useful when data sets are very large and the realm of queries is finite. Indexes are commonly used in relational database management systems but have never caught on in the decision support-oriented Hadoop world, where large-scale data ingestion is considered more important than rapid retrieval. Query tools such as Apache Hive support indexing, but the feature is not often used.

Jethro combines indexes with predefined views it calls “auto-cubes.” They’re based on the multidimensional cubes that are a common means of manipulating data in business intelligence scenarios. The platform generates aggregated multidimensional views of data based on frequent queries and stores them on the data warehouse or Hadoop cluster for rapid retrieval. Jethro can infer the need for cubes from query frequency, Singer said. For example, if queries commonly include U.S. state information, Jethro will pre-define cubes for all 50 states.

Cubes and indexes complement each other, Singer said. “The cases in which cubes are inefficient are the areas where indexes are more efficient, such as filtering across five different columns.”

The new version 3.0 greatly expands the range of queries that can support auto-cubes, and cube creation is now fully automated. “We previously supported a very narrow range of queries,” he said. Cubes are now also incrementally updated, which means that new data is assimilated into existing cubes without a complete rebuild.

Security features have been boosted through integration with Lightweight Directory Access Protocol and role-based permissions can be assigned in conjunction with Apache Hive and Ranger. A command line management interface has been replaced by a graphical user interface, and native integration with Hive now enables Jethro to index data directly from Hive tables.

Founded in Tel Aviv, JethroData put down U.S. roots in 2012. It has raised $15 million in venture funding and has about 20 paying enterprise customers, Singer said. The software is priced at approximately $30,000 per Jethro server, and the average customer runs about five servers.

Image: JethroData

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