Data Engineering, simplified

Yes. Platform is cloud native and supports variety of clouds. You can choose the cloud strategy that best chooses you - Your Private Cloud, Any of popular Public clouds (AWS,GCP,Azure) or any hybrid clouds .

Solution supports a wide range of sources and destinations, including both structured and unstructured data. Some examples of the types of sources and destinations that our platform supports include:

• Relational databases (e.g. MySQL, PostgreSQL, Oracle,ClickHouse)
• NoSQL databases (e.g. MongoDB, Cassandra,Hbase)
• Data warehouses (e.g. Redshift, BigQuery)
• Cloud storage (e.g. S3, Azure Blob Storage)
• Flat files (e.g. CSV, JSON,Parquet)
• Message brokers (e.g. Kafka, RabbitMQ)

We are continually adding support for new connectors, explore our detailed library.

Our platform's CDC capabilities allow it to detect and capture data changes made to a variety of sources, including relational databases and message brokers. Once the changes have been captured, they can be processed and transformed as needed before being loaded into the desired destination. This enables our platform to support real-time data integration and synchronization across systems, helping you to manage and maintain up-to-date data.

Yes. ETL chaining is a very useful feature platform offers. You can link multiple pipelines to be triggered sequently or in parallel to achieve seem-less orchestration across your data journey. Another advantage is that it allows you to create reusable transformation pipelines and utilize them as and when needed

The inbuilt observability layer provides a range of tools and features that enable you to monitor the status and performance of your pipelines in real-time, identify issues and bottlenecks, and take corrective action as needed.
Some examples of the types of data and metrics that our observability layer can provide include:

• Pipeline execution times and performance metrics
• Data quality and integrity checks
• Error logs and stack traces
• Resource utilization and resource allocation
• Custom metrics and alerts
• By providing this level of visibility into your data pipelines, our observability layer helps you ensure that they are running smoothly and efficiently, and helps you identify and address any issues that might arise. This enables you to get the most value out of your data and make more informed decisions based on the insights you uncover.

Our unified data engineering platform has been designed to be user-friendly, with an intuitive interface and a range of tools and features that make it easy to use, even for users without technical expertise.

• A drag-and-drop interface for building and managing ETL pipelines
• A wide range of pre-built connectors and transformation functions that allow users to quickly and easily extract, transform, and load data
• A range of visualization tools and dashboards that make it easy to view and understand data trends and patterns
• Context-sensitive help and documentation that makes it easy for users to learn and get assistance as needed
• Overall, our platform has been designed to make it easy for users of all skill levels to get up and running quickly, and to be productive with minimal training or technical expertise. This makes it a great choice for organizations looking to streamline their data engineering processes and get more value out of their data.

Apache Spark is a powerful open-source data processing engine that is widely used for large-scale data processing and analysis. In our unified data engineering platform, Apache Spark is used as the transformation engine, which means that it is responsible for performing the data transformation and processing tasks that are required to move data from sources to destinations.

Some examples of the types of transformation tasks that Apache Spark can perform include:

• Data cleansing and formatting
• Aggregation and summarization
• Joining and merging data sets
• Applying custom transformation functions
• Enriching data with additional information
• Apache Spark is designed to be fast and efficient, making it well-suited for handling large volumes of data and performing complex data processing tasks. Its distributed architecture also makes it highly scalable, allowing it to handle increasing data volumes and workloads with ease.

Our unified data engineering platform has been designed to handle large scale and multi-cluster environments, making it a suitable choice for enterprise-level deployments. The platform is capable to orchestrate the pipeline on different k8s clusters that ensure high availability and geo redundancy at same time also enabling it to efficiently distribute data processing tasks across multiple clusters. This ensures that our platform can effectively handle the demands of even the largest and most complex data engineering projects.