Why you need Elassandra to leverage your huge data stack

data bulk


Every business organisation, right from a startup to a multinational company, strives to gain actionable insights from their data. The preference is to have a user-friendly system that runs fast-paced searches across vast databases and yields error-free results. If the existing database management systems do not run smoothly, then a massive amount of time, resources and funds are spent on fixing it. This translates into losses, increased overheads and lack of efficient operations. Therefore, it is imperative to have a database management system that offers scalability, real-time search and zero rates of failure.

Elasticsearch is a search engine that offers real-time and full-text search with an HTTP web interface. Given its reliability, it is one of the most popular used by enterprises. Elasticsearch allows you to explore data trends and patterns and offers instant results. Let us have a look at Cassandra, the benefits that it offers include supporting huge volumes of data, no single points of failure, data is written in many locations and the most important feature i.e. it supports read and write scalability.

When we combine Elasticsearch and Cassandra, it blends the powerful and fast Elasticsearch engine with scale and resilience of Cassandra that ultimately transforms into Elassandra. It is the integration of Cassandra and Elasticsearch that results in highly available database center search and secondary index data store. Let us have a look at the benefits of integration of Elasticsearch and the Cassandra database system.

As far as data retrieval is concerned, Cassandra offers a fairly basic store of queries, and even though recently there has been an addition of extra functions, it is limited in comparison to Elasticsearch. Hence, when we combine Cassandra and Elasticsearch, it results in powerful and flexible querying.

With Elasticsearch, during the re-indexing and mapping, you must make allowances for downtime. But Cassandra lets you eliminate downtime during mapping as well as re-indexing.

Elassandra is still the same as Cassandra. Therefore, one can utilise and sync it with Cassandra’s database system. The applications as well as tools that work in sync with Cassandra will also work with the Elassandra system. For instance, tools like Kakfa, Spark and Storm will work well with Elassandra’s Cassandra API.

Similarly, Elassandra is also Elasticsearch. It supports the API’s of Elasticsearch and tools like Spark, Kafka, Kibana and so on, whilst also working along with Elasticsearch’s API. As far as data loading is concerned, you have the flexibility to choose from either API’s. i.e. Cassandra or Elasticsearch. Whether you select to load from Cassandra or Elasticsearch, Elassandra stores data in Cassandra’s table and it is indexed in Elasticsearch. You can use different languages like C++, Ruby and Java.

Elassandra or Cassandra + Elasticsearch do away the need of multiple clusters; instead of a Cassandra cluster, an Elasticsearch cluster and an ETL processes to replicate and synchronise between each cluster. So, once you load the data into the cluster of Elassandra, you do not have to load data into Cassandra, plus you can search for it directly.

Elasticsearch has a masternode and it has a single point of failure that needs to be managed with extra care and effort. Given the solid and masterless architecture of Cassandra, when you run Elasticsearch, there is no single point of failure.

Other benefits

Elasticsearch indexes can be replicated across different Cassandra datacenters, and while you can write to the closest datacenter, you can search globally. Cassandra can be your single datastore for indexed as well as non-indexed data. It is secured and reduces storage space, too.

In a nutshell, Elassandra offers you the best of both the worlds and eliminates hassles. Thereby, providing a one-stop solution for all your database management needs.


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