In a recent blog post Graeme showed how to create external tables in Kognitio that use tables in Hive, and how to speed up queries from Kognitio simply by creating partitions in Hive tables on columns used for filtering.  This post demonstrates a new feature in Kognitio’s upcoming 8.2.3 release where it can automatically filter on a partition column and eliminate partitions for a query with a filter on a “natural” dimension column, if the partition column value is derived from the dimension column. While Hadoop and Hive are used as examples in this post, the techniques discussed are equal ..read more

Last month, the merger of Cloudera and Hortonworks signposted a new era in the data market. But what exactly does two of the fiercest data rivals joining forces indicate? Does the Hadoop market need to be a little less competitive to survive? Are Hadoop’s days numbered? Is there a bigger competitor to overcome? Certainly, the big data market — and corresponding customer needs — have evolved since Hadoop became synonymous with Big Data. In the years since the two companies were founded, the landscape has changed significantly, and now includes a large number of companies with different strength ..read more

In Kognitio version 8.2.2, you can use our new Hive connector to make your Hive tables appear as Kognitio tables. Kognitio fetches the data directly from its source rather than pulling it through Hive, cutting out the middleman. A simple example We create a table in Hive, in this case using Hive’s client tool beeline: 0: jdbc:hive2://localhost:10000> create table hello_world(foo varchar(20), bar varchar(20)); No rows affected (1.479 seconds) 0: jdbc:hive2://localhost:10000> insert into hello_world values ('Hello', 'World'); No rows affected (12.455 seconds) Then we create an external t ..read more

This blog will take you through the new features in the latest Kognitio release, version 8.2.2, which is now available for download. Version 8.2.2 was designed around feedback from Kognitio users to resolve some key challenges they were experiencing. The release was designed with these goals in mind: Make it easier to connect to Hive tables Our users wanted us to simplify the process of bringing Hive tables into Kognitio, reducing the number of steps involved and reducing the management overhead. Improve the running of queries directly against external dataKognitio’s primary use case is build ..read more

This blog has been updated to reflect the reduced prerequisite requirements for Kognitio on Hadoop versions 8.2.1 and later. In this article we will install and configure a Kognitio cluster on to a running HDP Hadoop cluster. In order to run Kognitio on HDP Hortonworks you will need: Version 8.2.1-rel180801 or later of the Kognitio on Hadoop software (from https://kognitio.com/products/kognitio-on-hadoop ) You do not need a licensed Hortonworks installation to run Kognitio. Getting ready to start When running on Hadoop, the Kognitio software tarball only needs to be installed on one Hadoop ..read more

AVRO support was introduced in Kognitio 8.2 and in this post I’ll be briefly going through what it is and how to load it from HDFS. This is for Kognitio on Hadoop, if you need to install it see the install guide here. So what is AVRO? AVRO is an open source serialization framework for data held in Hadoop taking data and translating it into binary including the data definition or schema, defined in JSON, in the same file. This not only makes the data much more compact but also more efficient as you don’t just have the data but also how to read it as well. A key feature of AVRO is schema evoluti ..read more

This is an updated version of the blog entry first published in March which reflects the simplified installation procedure for Kognitio on Hadoop version 8.2.1 on Azure HDInsight… HDInsight is the easiest way of getting a Hadoop cluster up and running on Microsoft’s Azure cloud platform. It’s essentially Hortonworks HDP integrated with the Azure storage options. Installing Kognitio on HDInsight is pretty straightforward and can be accomplished by following the instructions here. This blog just provides some surrounding context for Azure. First create an HDInsight cluster of the type “Hadoop ..read more

Volume, variety and velocity are the three known defining properties that put the ‘big’ in ‘big data’. Hadoop helps businesses overcome many of the challenges of dealing with these three properties, but does it solve all of them? Does velocity mean faster analysis? While data is constantly collected from hundreds of sources at speed does Hadoop’s strengths include faster querying of your big data? Hearing “velocity”, you’d be forgiven for thinking it not only refers to fast data in but fast data out i.e. high-speed analysis; in reality querying your data on Hadoop is anything but. Traditionall ..read more

If you’ve just downloaded Kognitio on Hadoop, you’re probably wondering how the software will complement your existing BI tools, and support your fellow business users. The next step is getting your technical team on board, so they can install it onto your existing infrastructure. The below facts and stats will give your engineers all the information they need to understand the vast benefits of using Kognitio. Whilst using self-serve tools, chances are you’ve experienced speed and agility restrictions with high-user concurrency. SQL on Hadoop engines can vary widely in their ability to support ..read more

The MapR Hadoop distribution has unique benefits and extra capabilities that make it a desirable choice for many companies. Our new version of Kognitio, specifically for MapR, takes full advantage of MapR’s improved file system technology by accessing MapR POSIX APIs instead of HDFS API. Kognitio’s SQL engine for MapR offers a real enterprise-level alternative to using Apache Hive or Drill on MapR. Kognito’s analytical platform allows you to give wider access to data held in MapR, allowing self-service analytics users to query data using their preferred BI/Analytics tools and for data scientis ..read more