Cloud Migration

The Challenge

In the digital age big data and Analytics are widely being talked about. The efficient integration of mainframe data has multiple reasons: bigdata, workload relief of the mainframe to reduce costs, application modernisation, and mainframe to cloud, together with data and application migration without a “Big Bang”.

Declining mainframe know how, databases and their complexities grown over decades – these are big challenges when it comes to data exchange in a mainframe environment.

Many corporations used FTP or other file transfer solutions to resolve the data exchange problem. This is extremely CPU intensive, insecure and creates high costs for the distribution of mass data.

Nowadays these solutions are neither acceptable nor practicable anymore. Not only because of the costs, but also because of limited batch windows.

The Solution

There is a better solution to manage and master this complex and ambitious task in an easy, fast, reliable and efficient way: tcVISION for the timely, bidirectional data synchronisation and replication based on changed data. tcVISION turns the data exchange into a single-step operation. No middle-ware or message queueing is required. The data is exchanged in raw format, compressed and reduced to the processing of changed data.

Benefits

The focus is on changed data (Changed Data Capture), so the data transfer volume is reduced to a minimum.
Highest topicality through continuous real-time processing
Cost reduction through relocation of data exchange processes from the mainframe to cost efficient platforms (e.g. MS Azure, amazon cloud, others)
Cost reduction through compressed data transfers
High integration potential of the tcVISION solution: Multiple Change Data Capture technologies can be used depending on change frequencies and latency times
Comfortable data mapping
Maximise the use of your Data Warehouse
Prevention of mainframe costs: Integrated data repository creates transparency for all available data
No middle-ware required – elimination of costs and implementation efforts – efficient transport layer
Elimination of programming efforts for data transfers from change data sources
Comprehensive conversion of historically developed data structures
Integrated pooling/streaming processes avoid programming efforts and message queueing to prevent data loss because of unavailability of the target system or delays
Processes which have proven to work in practice are available to restart a replication after system failures (database errors, transmission errors, etc.)

Application Examples

Data Synchronisation in a heterogeneous system environment consisting of a mainframe and mobile applications which make use of data from cloud solutions
Gradual migration of data and applications in in cloud based solutions
Transfer of database data to various databases hosted by the cloud solution
Provision of mainframe data in cloud systems for Data Warehousing, ETL, Business Intelligence, Analytics & big data

Intuitive mapping offers comprehensive possibilities for data modeling and a variety of integrated functions for the transformation of data types between mainframes and databases in – in this example here: MS Azure – including for example packed fields on the mainframe. The complete change of the data model is no problem for tcVISION – even in a bidirectional replication. See tcVISION Frequent Questions.

Cloud Solutions with tcVISION

Available for Windows, Linux and UNIX, depending on the cloud system
Databases supported in cloud systems: IBM Db2 LUW, IBM BLU Acceleration, IBM Informix, IBM NETEZZA, Oracle, Sybase, Microsoft SQL Server, Software AG ADABAS LUW, PostgreSQL, Teradata, MongoDB, Flat File Integration, SAP Hana, EXASOL, MySQL / MariaDB, JSON / Avro, ODBC, Kafka, Hadoop Data Lakes, HDFS, CSV
Provides a fast and efficient means of maintaining your data warehouse
Near real-time processing of the active or archived log files to capture the changed data
Change capture processing uses standardised interfaces
Support of all Change Data Capture methods:
- DBMS Extension
- Log Processing
- Batch Compare
- Loads
Comprehensive mapping functions for the creation of structure information for all supported databases running in the cloud solution
Central, relational repository for storage of the metadata, linkage and processing rules
No additional middle-ware required
No message queueing required
Compressed and efficient data transfer
Restart/Recovery guaranteed after system failures
Support of DBMS backup formats (archive log files) for the efficient exchange of bulk data without the necessity of accessing production data
Support of unidirectional, bidirectional and 1:1, 1:n, n:1 and n:n replications
Convenient single-step-operations. Capturing of change data – transformation of data – application to target systems
Integrated workload balancing to shift tasks like processing and conversion to more cost-effective systems (e.g. from mainframe to cloud system).
Parallelisation of load processes in order to realise real low latency synchronisation solutions
Integrated features for direct application of data into target systems
Powerful load functions to transfer and apply bulk data to the cloud or other data destinations
Integrated pooling and streaming processes prevent data loss if the target cannot receive data or receives slower than the source is sending.
Integrated loop-back prevention for bidirectional data exchange
Built-in key management for non-indexed data
Extensive monitoring, logging and integrated alert notification