What is DataOps?
DataOps is an automated, process-oriented methodology to improve the quality, speed, and accuracy of analytics. It helps with your data management from getting much more valuable data leads that turn into better insights.
Data Integration Platform
Do you need a solution to manage your data effectively and efficiently? Are your pipeline performance tuning and optimisation not up to speed?
It is often that an enterprise’s IT system is based on loosely coupled cloud applications which requires double entry of data. This leads to delayed exchange of data, the cost of which is sometimes high for the overall business.
How can Technaura help with data integration platform?
Technaura has built a unique data integration platform that helps you connect all your coupled cloud applications and share the data in real-time. With Kafka at the centre of it, this system is cost-effective and often does not require any infrastructure at the client-side. This system is available at all major cloud providers. Therefore, it does not need a notable change in your current IT system.
Data Streaming Pipeline
Are you considering replacing your legacy batch applications with streaming solutions to get real-time data and a more reliable platform at lower cost?
Technaura offers streaming data pipeline solution that connects data sources and destinations via Kafka Connect without any involvement of code. The changes get identified from the database log/commit file (CDC).
Here, we have multiple sources of data, i.e. streaming sources, SQL databases, NO SQL databases, and we have a single destination for saving all records.
Create your DataOps solutions
We created a streaming pipeline using Kafka and Kafka Connect in which we can integrate multiple sources like SQL databases, public streaming sources, etc. We also included a microservice for aggregation operation of the data received. The data pipeline will sink the respective data in an Elastic Search cluster.
The whole flow is managed by Kafka Connect and custom connectors with zero lagging expectations. The stored data for different indexes can be seen/analysed via the Kibana dashboard.
Any change in the tables will be captured by the Source connector. The Elastic Sink Connector will then save/dump the changes to the destination.
Data from streaming source will be published to Kafka by aggregator microservice publisher. The Elastic Sink Connector will update the data to the Elastic Search where it is stored. Visualisation of real-time data is then done in Kibana.
Furthermore, from the concept, we can integrate an 'n' number of sources (PostgreSQL, Mongo, Couch, or Cassandra) and have all the results in the Elastic search / Kibana dashboard.