Why people prefer to use Hadoop or Spark when there is Elasticsearch?
When traditional ways are not able to handle, Hadoop development services helps in handling massive data within a fraction of seconds. The support of multiple machines is required to handle the process parallelly in a distributed way. Elasticsearch analyzes the data that is fetched by Logstash, and then Kibana gives the insights out of it. However, people tend to use Hadoop or Spark even when there is Elasticsearch with such features. Let us know about the reason behind people preferring Hadoop over Elasticsearch.
- While NoSQL and Hadoop technologies make it easy to upload data, it is not the same as Elasticsearch. Before being uploaded, Elasticsearch recommends the data to be transformed into generic key-value pairs. If you don’t do this, for every key-value pair, Lucene will create an index causing the size of your Elasticsearch to explode eventually. When working through millions of documents, these conversions act like an excellent resource hog.
- Although Elasticsearch had expanded beyond the search engine and added features for visualization and analytics, it remains a full-text search engine. It provides less support for complex aggregation and calculation than Hadoop or Spark. Hadoop provides a powerful and flexible environment. Since Spark is derived from Hadoop, it comes with the same features.
- Hadoop/Spark and Elasticsearch may overlap on some useful functionality. If you want to perform simple analytics and search documents by keyword, Elasticsearch might fit just rightly. But if you are dealing with a massive amount of data and it calls for different types of complex analysis and processing, then Hadoop serves the most flexibility and broadest range of tools.
- In short, it ultimately depends upon your needs. Want to search well-defined data? Elasticsearch would do the work. But if you want to run complex data processing, you have to go with Spark/Hadoop. Both of them can run python, java, Spark code, to digest any type of data. In Elastic, you are not allowed to write any code; you have to remain satisfied with a query.
- Both Hadoop and Spark has domain-specific libraries to deal with a massive amount of data. If you require to diversify data from multiple sources with efficiency and flexibility, then Hadoop/Spark could be your choice. There are some things which the Elasticsearch cannot do that Hadoop/Spark can, which is why people tend to prefer the latter option.
- When it comes to streaming ingestion, many struggles with the limitations that come with ElasticSearch consulting. If between nodes, a network outage cuts connections, it’s known to cause an issue. The severing of connection is known to cause 100% loss in the streaming of data. Once you miss some data, it’s gone. So if you want data integrity, it would be wiser to store data on Hadoop/Spark to run analytics.
There is a reason why Hadoop is go-to-tool for many people. HDFS prevents data loss and makes the system highly fault-tolerant. Also, it possesses many tools for supporting bulk upload, SQL engines for querying data and data ingestion. It has the ability to handle any data aggregation. Though it comes with heavy setup and domain-specific knowledge, choosing Hadoop is a win-win for everyone every time.