Data Management - Collect process & analyze data
- Many connectors to import and export data
- Drag and drop real time/batch pipeline designer
- Prepare, ingest and transform data quickly
- Explore, Reconcile and manage SLA
Insight Lake data management service enables data administrators to ingest data from various sources, validate, standardize, clean, enrich and process to provide value to business users.
Ingest data in minutes from anywhere and any type without writing code. Prepare, cleanse, and enrich using code-free data wrangler and built-in transformation plugins. Blend data from traditional RDBMS to Data Warehouse to Hadoop.
Data management service UI provides drag and drop visual designer, where components can be dragged from the palette and connected together to form complex data pipelines.
Data pipeline components are classified in following categories:
Created data workflows can be scheduled to run always or at specified schedule. Flow explorer shows the running data jobs and their operational data gets captured for Ops teams exploration.
Ingested or transformed data stored at any location like Kafka, HDFS, S3, Hive, MySQL, Oracle, SOLR etc can be explored easily using an intuitive data explorer.
Explorer also provides features like highlighting rows/cells, adding tags and notes, which could be used for capturing additional information about data at cell level.
Ingested or transformed data can be easily visualized by creation of dashboards using Dashboard service. Basic charts like Histogram, Pie, Line, Donut, Tables, Maps etc can be easily created. Complex charts like Tree, Summary, Pivot, Linked tables, Force directed graphs can also be used.
Charts could be built on top of any data source like Kafka, Database, SOLR, Elastic Search, Hive etc.
Realtime charts could be used for operational dashboards.
Data Reconciliation feature enables comparison of two data sets, which results in matching and non matching records. Data reconciliation can be done at various places, for example:
1 Source to target matching - In a data pipeline end step could be provisioned to check if target data store reflects all the source records. If records match that means data pipeline has properly processed all the records, otherwise something went wrong during the process.
2 Matching records from different data sources - Data validation/reconciliation can be done between two data sources, which could be built by separate data pipelines.
Data reconciliation can be automated to run at pre defined period to check the data consistency.
Drill down feature allows checkin what parent table records were used in formation of subject table's mismatched record. This helps in finding the gaps easily.
Data adjustment feature allows adjustment/update capability using UI. It also captures audit information like who changed, when, what changed, why change was done and who approved the change.
All changes related to data like data pipeline flows, reconciliation jobs, adjustments etc follow an approval/governance process where data steward/admin reviews and approves the change then only effect is materialized.
Data management allows migration of data pipeline templates, reconciliation jobs from lower environment to upper like DEV to QA to PROD by following approval process.