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Apache Spark Consulting and Implementation

apache-spark-consulting.jpgApache Spark uses in-memory processing to provide a fast and easy way to run interactive analytics on large datasets. Spark can deliver queries up to 100 times faster than other big data processing tools, such as Hadoop. And, as an open source Apache project, Spark provides a cost-effective way to conduct real-time analytics and business intelligence.


COST-EFFECTIVE, REAL-TIME ANALYTICS ON LARGE DATASETS 

apache-spark-logistic-regression.pngThe benefits of leveraging Spark include:

  • Large-scale data processing:

- Built to be distributed
- Built for large-scale linear scalability
- Creates and combines massive distributed data sets with a single line of code

  • A rich open source environment that provides:

- Many functions, libraries, and operators
- Many contributions from the community
- API integration with Scala, Java, Python, and R 
- Can access diverse data that reside in HDFS (Hadoop Distributed File System), Cassandra, HBase, and S3 (Amazon Simple Storage Service)
- Can run as a standalone application, on Hadoop and Mesos, or in the cloud.

  • High-level operators are more easily optimized:

- Old-style parallel processing programs are complex, difficult, and time-consuming to scale
- MapReduce lacks data semantics, making it harder to optimize performance

OUR BIG DATA ANALYTICS EXPERTISE

  • Spark consulting and assessments - evaluating your data architecture and application requirements, and creating a blueprint for implementation
  • Design and implementation – integrating Spark into your existing system 
  • Content processing – proven technology assets to prepare, normalize, and enrich structured and unstructured data 
  • 24x7 support and managed services – to ensure your analytics application runs smoothly so you can focus on your business objectives

APACHE SPARK USE CASES

Our Apache Spark consulting and implementation expertise has helped commercial, government, and research organizations efficiently process, search, analyze, and visualize large datasets that amounted to petabytes (genomics, social media, email and voice communications, and online activities, for example) 

We've leveraged Spark in a wide range of real-world big data analytics use cases, such as:

APACHE SPARK REFERENCE ARCHITECTURE

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Contact us for a discussion on how Spark can add value to your BI and analytics initiatives and how we can help with the implementation. 

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