by  Taras Bachynskyy

Big Data Warehousing with Elasticsearch

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Data processing is more than just storage, consolidation, or aggregation, and even the "4 Vs"do not always cover all of the challenges associated with modern Big Data solutions. Read on to find out to address the technical challenges and achieve a Big Data project’s defined goals using Elasticsearch.

A Matter of Choice

Today there are many technologies and approaches available for Big Data solutions, so it comes down to the choice of the right architecture and technology for a given project.

Elasticsearch is not just a search engine; thanks to aggregates, it may also serve as a place to store reports and as a computing engine, especially when search-based filtering and aggregations need to be combined. Here are some of its benefits:

  • Near real-time processing with 5 second latency
  • Low-latency aggregations over terabytes of data
  • High throughput of tens or even hundreds of thousand messages per sec.
  • Hundred(s) of concurrent users

But since there is no such thing as a one-size-fits-all solution, let’s explore when Elasticsearch is effective and when it is not applicable by comparing it with Redshift as a data warehouse choice for near-real time scenarios.


Initiate a prototyping phase.

Finally, you should initiate a prototyping phase before planning implementation because technical risks on a Big Data project are always a factor – this is a reality you can’t forget or ignore.


Typically, the utilization of traditional approaches is a “path of least resistance,” meaning it is less risky but can also be less effective in terms of achieving a project’s goals. Sometimes alternative solutions can best address your needs.

Curious how a data warehousing works in real-life application? Check out how SoftServe implemented a software solution to improve management, visibility, and service delivery for a leading client-centric global banking and financial services company in this case study.