Exploring Real-Time Data Replication with IBM Data Replication


Even though data lake may be all about Apache Hadoop, integrating operational data is usually very challenging a challenge, as it does not cope well much with changing data, due to lack of any concept of "update," nor “delete.”A Hadoop software platform can provide a proven economic, highly scalable and trusted means of storing volumnious data sets on commodity hardware.

The power of discovery that usually is present with a schema seems also missing, creating an obstacle to well understood transaction data integrating. This in turn is interfering adversely with easy storage of data in a relational database. Apache Kafka will facilitate making the most from your data, including structured business information and the very high volume and unstructured data from social media, internet activity, sensor data and as such.

It is a fact that Kafka is increasingly becoming the enterprise standard for information hubs that can be utilized with or to feed data to the data lake. It is highly suitable dealing with events and data that are prone to changes on a constant basis. Using commodity hardware for highly scalable and reliable storage, Apache Kafka can be righteously complimented as a better tool compared to Hadoop, as it comes featured with a schema registry, self-compressing storage that can comprehend the concept of a "key,” and other characteristics. These characteristics can well assume data will change.

An array of "writers" and "consumers" build to such open standard, ensuring and adding strength to integration of transaction and other quickly modifying useful data with enterprise data stores, processing platforms and more. As data is amassed in a Kafka-based information hub, Kafka consumers can feed data to the desired end points, including

  • Information server
  • Hadoop clusters
  • Cloud-based data stores


It has been found that a Kafka-consuming application is capable to perform analytics functions using the data amassed in the Kafka clustered file system itself or to trigger real-time events.
IBM Data Replication is known to provide a Kafka target engine,streaming data into Kafka using either a Java API-based writer with built-in buffering, or a REST (Representational State Transfer) API using batch message posts. This is being provided to

  • assist organizations deliver changing data into Hadoop-based data lakes or Kafka-based information hubs
  • Making greater amount of real-time data available to such enterprise data lakes or data hubs
  • Enabling enterprises capture information from source transactional systems with negligible impact
  • Deliver changes to analytics and other systems at reduced latency
  • Analyze voluminous amounts of data in motion.


IBM Data Replication is equally capable to deliver real-time feeds of transactional data from mainframes and scattered environments directly into Hadoop clusters, with its Hadoop target engine, using a WebHDFS interface.

IBM Data Replication solution can make it possible ensuring quick and accurate real-time incremental delivery of transactional data to Hadoop and Kafka based Data Lakes or Data Hubs with data replication. Making an exploration of real-time data replication with IBM Data Replication will be really a quality business decision.

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