Apache Spark Consulting, Implementation, Support and Fine-tuning

With a large experience in big data consulting, we know how to deliver a Spark-based analytics solution tailored to your needs. Our consultants are ready to support you at any stage of your big data journey, efficiently tackling the challenges you may encounter on the way.

DataBeans’s expertise covers a wide range of big data technologies, such as Apache Hadoop, Apache Hive and Apache Cassandra, but among big data processing frameworks, Apache Spark is the one we cherish the most.

Spark Apache

Consulting on big data strategy

Our consultants bring in their deep knowledge of Apache Spark, as well as their hands-on experience with the framework to help you define your big data strategy. You can count on us when you need to:

  • Unveil the opportunities that Apache Spark opens.
  • Reveal potential risks and find ways to mitigate them.
  • Select additional technologies to help Spark reveal its full capabilities.

Consulting on big data architecture

With our consultants, you’ll be able to better understand Apache Spark’s role within your data analytics architecture and find ways to get the most out of it. We’ll share our Spark expertise and bring in valuable ideas, for example:

  • What analytics to implement (batch, streaming, real-time or offline) to meet your business goals.
  • What APIs (for Scala, Java, Python or R) to select.
  • How to achieve the required Spark performance.
  • How to integrate different architecture elements (Spark, a database, a streaming processor, etc).
  • How to build Spark applications architecture to facilitate code reuse, quality and performance.

Spark fine-tuning and troubleshooting

Apache Spark is famous for its in-memory computations, and this area is the first candidate for improvement, as the memory is limited. You don’t get the anticipated lightning-speed computation and lots of your jobs are in the waiting status, while you are waiting for analysis results? This is disappointing, yet fixable.

One of the reasons can be a wrong configuration of Spark that makes a task require more CPU or memory than available. Our practitioners can review your existing Spark application, check workloads and drill down into task execution details to identify such configuration flaws and remove bottlenecks that slow down the computation.

No matter what problem you experience – memory leaks due to ineffective algorithms, performance or data locality issues or something else – we’ll get your Spark application back on the rails.