A great part of the added value of modern IT systems is the latency-free data and process integration of transactional and analytical areas. The cross-system integration platform tcVISION is unique, efficient, and reliable. With tcVISION, mainframe data can be fast and easily integrated into Apache Kafka based operative applications or Business Intelligence and Analytics in near real time.

The tcVISION solution is practice approved and is constantly further developed to meet the requirements of the new technologies. As a result, Apache Kafka is a fully integrated output platform in the current version of tcVISION V6.

Apache Kafka is best suited to meet the challenges of big data requirements since it is a distributed system and real-time scalable.

As with all output platforms provided by tcVISION, the data streaming via Apache Kafka is based on official standard interfaces. The implementation of the Apache Kafka interface is fast and easy.

Data streaming to Apache Kafka as a transport layer enhances the Big Data connectivity of tcVISION.

The currently used protocols for data transfer to Apache Kafka are JSON and AVRO. The main focus of the tcVISION integration platform is the supply of a real-time synchronization to integrate mainframe data into Kafka based solutions.

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The tcVISION integration platform consists of a variety of state-of-the-art technology components which cover far more than an ETL process.

  • Data exchange in the sense of real-time synchronization and replication turns into a single step operation with tcVISION.
  • No additional middleware is required.
  • Diverse Change Data Capture technologies allow an efficient selection of the required data from the source system with focus on the changed data. The data exchange process is reduced to the necessary minimum which results in lower costs for the data exchange.
  • tcVISION can also use backup and recovery files (e.g. imagecopies, log files, UNLOADs) instead of production data as a source for replication.
  • tcVISION enables the fast and efficient load of large volumes of mainframe data into Apache Kafka (streaming). The processor costs of the mainframe are very low.
  • An integrated data repository guarantees transparent data management across platforms.
  • Mainframe knowledge is not necessarily required for the replication.
  • tcVISION includes a rule engine to transform data into a target compliant format or allows user-specific processing via supplied APIs.
  • The integrated staging concept supports the offload of changed data in “raw format” to less expensive processor systems. This reduces costs and mainframe processor resources to a minimum.
  • The preparation of the data for the target system can be performed on a less expensive platform (Linux, UNIX, or MS-Windows).
  • The transfer to and streaming of data into Apache Kafka is part of the tcVISION data exchange process. No intermediate files are required.
  • The exchange of large volumes of data between a production mainframe environment and Big Data can run in parallel processes to reduce latencies to a minimum.
  • The tcVISION integration platform contains comprehensive control mechanisms and monitoring functions for an automated data exchange.
  • tcVISION has been designed in a way that Kafka based projects can be deployed with complete project autonomy and maximum reduction of mainframe resources.
Supported Kafka & Confluent Features
  • tcVISION uses Kerberos for the authentication process in Kafka.
  • tcVISION supports SSL to communicate with Kafka
  • tcVISION utilizes the Confluent Schema Registry to register schemas.
Supported CDC Sources for tcVISION
IBM Z SystemsLUW Platforms
  • Db2 z/OS*
  • Db2 z/VSE
  • IMS/DB
  • DL/I
  • VSAM
  • CA Datacom
  • Adabas
  • Db2 LUW*
  • Db2 BLU*
  • Adabas LUW
  • MS-SQL Server*
  • Oracle*
  • MySQL*
  • MariaDB*
  • PostgreSQL*
  • Informix

* agentless possible

  • Near real-time replication of mainframe data to Big Data allows actual real-time analytics.
  • The relocation of mainframe applications (e.g. internet applications, such as online banking or e-Government) to Big Data with synchronous data on both platforms is also possible.
  • The costs of the data exchange are reduced to a minimum through the concentration on changed data.
  • The utilization of mainframe resources is reduced as far as possible to avoid costs for mainframe knowledge and mainframe MIPS.
  • Data exchange processes can be deployed and maintained with tcVISION without mainframe knowledge, thus costs can be saved and Big Data projects can be developed and put to production faster.
  • The near real-time replication of tcVISION from mainframe to Big Data allows the relocation of BI, reporting, and analytic applications to the more cost-efficient and – for these applications – more powerful Big Data platform.
  • Compensation of decreasing mainframe knowledge.
See how tcVISION works with Kafka:
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