Data Engine

Sapper enables making right data available for right workloads at right time to get right insights and take right decisions

Sapper – Legacy Modernization Platform

Rich Library of Data Sources

Out of the box integrations with many applications, databases, streaming services, and various other sources including Hadoop Distributed File System (HDFS). All standard connection methods and data formats are supported.

Common IT Challenges Solved by Sapper

Hybrid Cloud Integration

Enterprise Data Lake

Migration

Realtime Reporting

Geographical Distribution

High Availability

Key Features

Intrinsic support for batch processing, streaming & Change Data Capture (CDC)

Guaranteed Delivery and Transactional Consistency supported where required.

DSL driven Data Validation and transformations

Scaled to handle 1 Million+ events per minute for both Ingress and Egress

Extensible framework for building custom connectors

Integrated authentication and authorization

Change Data Capture (CDC)

Sapper uses the CDC Data Integration approach that allows high-velocity data to achieve reliable, low latency, and scalable data replication using fewer computation resources. Change Data Capture (CDC), enabled companies to deliver new data changes to BI (Business Intelligence) tools and team members in real-time, keeping them up to date.

Companies need access to real-time data streams for Data Analytics. Sapper mainly uses Log-Based CDC to ensure no performance impact on the sources. Only the changed data is transferred for faster performance. CDC excludes the process of bulk data loading by implementing incremental loading of data in nearly real-time.

Sapper CDC engine provides you a rare ability to read newly written data from HDFS volumes and sync it to any target to save a considerable amount of time.

CDC Connectors

Hadoop

Oracle

PostgreSQL

MongoDB

MySQL

SQL Server

DB2

CDC Features

Snapshot: Initial snapshot of a database’s current state can be taken if a connector is started and not all logs still exist. Typically, this is the case when the database has been running for some time and has discarded transaction logs that are no longer needed for transaction recovery or replication. There are different modes for performing snapshots.

Filters: You can configure the set of captured schemas, tables and columns with include/exclude list filters.

Masking: The values from specific columns can be masked, which contains sensitive data.

Monitoring: Most connectors can be monitored by using JMX.

Message transformations: Message transformations can be done for Message routing, Content-based routing, and Filtering.
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Fully Automated and Reliable Data Pipelines for Faster Analytics

Sapper’s fully managed and automated data pipeline loads all your data to the warehouse or data lakes at scale in real-time, ready for analysis.

Data Delivery Assurance

Just configure the source and destination. Sapper will ensure timely and accurate data delivery from source to target.
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Easy to Use

Anyone can easily build pipelines with our simple drag and drop features. Your pipelines will be ready in no time, just conceptualize and start building.
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Highly Configurable

Sapper provides you the ability to configure the pipeline as per your needs. e.g., if you can stop data transfer on error or choose to ignore errors and load the valid data. There are loads of other useful configurations available.

Fast & Scalable

Near real-time delivery of data from source to target without affecting the source application performance. No dip in performance at peak load time too.
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No Code, Low Maintenance

You don’t need to maintain a highly skilled technical team to develop complex integrations. Adopt new platforms and technologies faster. It will be as easy as changing configurations.

Fault-Tolerant

If the source system fails in between, the state is maintained and once data transfer is resumed, it will be picked up from that point, saving you precious time and resources.