4 ways Elasticsearch can enhance Hadoop ecosystem

4 ways Elasticsearch can enhance Hadoop ecosystem
elasticsearch for hadoop

Unfathomable amount of data pouring in with high velocity have been forcing organizations to deploy distributed computing platform, especially Hadoop. As a de-facto platform for bigdata, Hadoop offers highly scalable and cost-efficient data store.

Since the beginning, Hadoop’s MapReduce has been a great tool for batch-oriented processing of huge amount of data. However, Hadoop was never considered good at realtime analytics. As the hadoop ecosystem is evolving, advanced tools such as Elasticsearch for hadoop connector, affectionately called eshadoop have been introduced to new opportunities of exploring your data in Hadoop ecosystem. Eshadoop connector allows you to let your data flow between Elasticsearch and Hadoop.

Elasticsearch is a distributed scalable real-time search and analytics engine. By using thoughtful architecture on whether to colocate Elasticsearch and Hadoop clusters or to maintain them separately you can gain following 5 major benefits.

1) Powerful Full-text search and analytics engine

Elasticsearch-Hadoop connector makes it possible for you to get all your Hadoop data from various Hadoop ecosystem tools like MapReduce, Pig, Hive, Cascading or Spark. Once you get the data in Elasticsearch, it opens up doors for full-text search through rick Query DSL by Elasticsearch. It helps you easily perform relevance search on massive amount of data. It also has rich aggregation capabilities for advanced analytics so you can make sense of your data quickly.

2) Lightning fast search speed on Hadoop data

The way storing and processing unstructured data is important, it is equally crucial to be able to search huge piles of data as quickly as possible. Especially when you have large number of users querying the data. Elasticsearch makes scalability seamless. Its distributed architecture backed by Apache lucene makes searches on large data lightning fast.

3) Ingest social data into Elasticsearch using Apache Spark or Apache Storm

Ingesting social data for analysis is a good way to get insight into current trends. With eshadoop it becomes great. You can gather streaming social data, classify them in real-time or perform sentiment analysis and import directly into Elasticsearch. Once you got them into Elasticsearch, you can perform complex full-text search queries, geo-analysis or analyse them to know trends that can be vital for your present or future marketing strategies.

4) Easy data exploration

Elasticsearch-Hadoop connector can leverage Hadoop ecosystem by letting you visualize huge volume data through interactive dashboards. This is crucial, as it it allows business analysts to explore data without relying on IT department. It enables you to ask the questions as it comes to mind and which you never thought of earlier.In other words, it makes your data fluid for business operations people to consume in near real-time. Combined with Elasticsearch, Kibana is all set to make a unique place in Hadoop eco-system as a data discovery tool for quick discovery and analysis of your data.

Krishna Meet is a technical writer and UX designer having core interest in analytical dashboard designing. Majority of her career span was into tech-writing. However, she thrives by intersecting multiple skill-sets : DITA documentation, script writing for tech-videos, business analysis, and UX design. She is a voracious reader and possesses Masters degree in Computer Science. Her interest in agile methodologies and user-centered design has landed her a techno-functional role at Brevitaz.

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