Historically, many orgs. have replaced data warehouses with Hadoop clusters and used Hive along with Impala (on Cloudera deployments) or Drill (on MapR deployments) for SQL. Hive is older and slower, while Impala and Drill are newer and faster, but you typically need both for their complementary features, at least today.
Spark and Spark SQL are not yet complete replacements for them, but they'll get there over time. The good news is, you can mix and match these tools, as appropriate, because they can all work with the same datasets. The challenge is all the tribal knowledge required to setup and manage Hadoop clusters, to properly organize your data for best performance for your needs, to use all these tools effectively, along with additional Hadoop ETL tools, etc. Fortunately, tools like Tableau are already integrated here. However, none of this will be as polished and integrated as what you're used to. You're trading that polish for greater scalability and flexibility. HTH. Dean Wampler, Ph.D. Author: Programming Scala, 2nd Edition <http://shop.oreilly.com/product/0636920033073.do> (O'Reilly) Typesafe <http://typesafe.com> @deanwampler <http://twitter.com/deanwampler> http://polyglotprogramming.com On Thu, Feb 26, 2015 at 1:56 AM, Vikram Kone <vikramk...@gmail.com> wrote: > Hi, > I'm a newbie when it comes to Spark and Hadoop eco system in general. Our > team has been predominantly a Microsoft shop that uses MS stack for most of > their BI needs. So we are talking SQL server for storing relational data > and SQL Server Analysis services for building MOLAP cubes for sub-second > query analysis. > Lately, we have been hitting degradation in our cube query response times > as our data sizes grew considerably the past year. We are talking fact > tables which are in 1o-100 billions of rows range and a few dimensions in > the 10-100's of millions of rows. We tried vertically scaling up our SSAS > server but queries are still taking few minutes. In light of this, I was > entrusted with task of figuring out an open source solution that would > scale to our current and future needs for data analysis. > I looked at a bunch of open source tools like Apache Drill, Druid, AtScale, > Spark, Storm, Kylin etc and settled on exploring Spark as the first step > given it's recent rise in popularity and growing eco-system around it. > Since we are also interested in doing deep data analysis like machine > learning and graph algorithms on top our data, spark seems to be a good > solution. > I would like to build out a POC for our MOLAP cubes using spark with > HDFS/Hive as the datasource and see how it scales for our queries/measures > in real time with real data. > Roughly, these are the requirements for our team > 1. Should be able to create facts, dimensions and measures from our data > sets in an easier way. > 2. Cubes should be query able from Excel and Tableau. > 3. Easily scale out by adding new nodes when data grows > 4. Very less maintenance and highly stable for production level workloads > 5. Sub second query latencies for COUNT DISTINCT measures (since majority > of our expensive measures are of this type) . Are ok with Approx Distinct > counts for better perf. > > So given these requirements, is Spark the right solution to replace our > on-premise MOLAP cubes? > Are there any tutorials or documentation on how to build cubes using Spark? > Is that even possible? or even necessary? As long as our users can > pivot/slice & dice the measures quickly from client tools by dragging > dropping dimensions into rows/columns w/o the need to join to fact table, > we are ok with however the data is laid out. Doesn't have to be a cube. It > can be a flat file in hdfs for all we care. I would love to chat with some > one who has successfully done this kind of migration from OLAP cubes to > Spark in their team or company . > > This is it for now. Looking forward to a great discussion. > > P.S. We have decided on using Azure HDInsight as our managed hadoop system > in the cloud. >
