Advanced ETL tools custom designed for the Snowflake Cloud Data Warehouse. It focuses on maintaining data quality, security, and governance. Rapid time to value.
Our customizable Smartdata Suite of Products is designed to seamlessly integrate with Snowflake and all its functions. Simplified ingestion of huge volumes of data from various types of sources including mainframes, databases, enterprise applications, warehouses, IoT, streaming and more.
The ChainSys Smartdata Suite can put forward data quality improvement suggestions, take inputs and correct your data, at any stage of the data lifecycle. This is made possible with a Low Code/No Code approach and pre-built adapters for your applications.
Snowflake’s platform stores data at scale within the cloud. ChainSys algorithms enable users to efficiently search for any data across a variety of data assets whilst maintaining our ease-of-use principles in line with our Customer-First Approach.
With AI/ML capabilities powering our products, detecting bad data, cleansing and maintaining high quality data with no redundancies and duplication is one of our many strengths. Automated processes make streamlining data and analytics extremely simple.
Governance and Data Management of an entire organizations data can become a highly strenuous task. With ChainSys’ Data Catalog bots that labor is minimized as they scrutinize and parse through SQL Query history to fully automate Data Lineage Construction and PII Detection. This ultimately translates to cost-effective and time-saving methods for dynamic policy creation and top-notch governance.
Our visual Query-Builder enables even non-technical users to directly query across across data lakes and warehouses to ensure high accessibility for data exploration.
Data Profiling does an examination of the source data and gives an insight into the source data information. It helps identify how the data is being used, the cleansing requirements, and how much data needs to be migrated. Profiling enables to find the Data Patterns, Erroneous Data, Inconsistent Data, and Incomplete Data.
The consolidation engine uses ML Algorithms to identify potential matches across the master data spectrum. The ML engine also has a flexible rules engine to accommodate the automate merging of the field values from the victim data over to the survived records.
Data Cleansing AI Rules Engine helps with Automated data Cleansing of the fields that are essential for the Business. It helps create a ‘Single Source of Truth’ for Hierarchical representation of master data, which is very important for providing powerful analytics and insights.
The Source master data, especially data from some legacy systems, may not have all the values needed for the New Modern target Applications. This calls for additional Enrichments of attribute columns which can be very helpful for Improved Operations, Reporting, and Analytics.
Data audit trail is maintained from the initial raw data to the final clean golden records. Useful reporting of Raw Data, Matches Data, No Matches Data, Survivors, Victims, Data Cleansing, Enrichments, and Gold Data is OTB