Although Kylin is an Apache project, it is not currently part of the Hortonworks Data Platform. It is important to mention that Kylin does limit your BI options somewhat, as it is oriented at Microsoft PowerPivot and Tableau. Alternatively, we at Hortonworks are engaging AtScale quite frequently with our customers in the field.
AtScale provide a unified Semantic layer, based on "Virtual Cubes". Virtual Cubes allow the user to create models with measures and dimensions, just like OLAP, but on large volumes of data stored in Hadoop. Users can ‘scale-out’ their BI, because the cube is ‘virtual’. Users can query multiple years, lines of business, brands, etc. all from 1 ‘virtual cube’, scaling out to millions and billions of rows of data available to query.
AtScale Adaptive Cache generates automatic ‘smart aggregates’ that learn to anticipate user BI and OLAP queries so you maintain scale, performance and control across your Hadoop cluster. AtScale can leverage any SQL engine under the covers (including Hive or Spark), as well as any BI tool (including those that require an MDX interface, rather than a SQL interface).
If not interested, I can dig further into Apache Kylin or Apache Lens for you - but your support options may be limited going forward until the respective communities around those projects begin to grow.
Although Kylin is an Apache project, it is not currently part of the Hortonworks Data Platform. It is important to mention that Kylin does limit your BI options somewhat, as it is oriented at Microsoft PowerPivot and Tableau. Alternatively, we at Hortonworks are engaging AtScale quite frequently with our customers in the field.
AtScale provide a unified Semantic layer, based on "Virtual Cubes". Virtual Cubes allow the user to create models with measures and dimensions, just like OLAP, but on large volumes of data stored in Hadoop. Users can ‘scale-out’ their BI, because the cube is ‘virtual’. Users can query multiple years, lines of business, brands, etc. all from 1 ‘virtual cube’, scaling out to millions and billions of rows of data available to query.
AtScale Adaptive Cache generates automatic ‘smart aggregates’ that learn to anticipate user BI and OLAP queries so you maintain scale, performance and control across your Hadoop cluster. AtScale can leverage any SQL engine under the covers (including Hive or Spark), as well as any BI tool (including those that require an MDX interface, rather than a SQL interface).
If not interested, I can dig further into Apache Kylin or Apache Lens for you - but your support options may be limited going forward until the respective communities around those projects begin to grow.
