Data Ingestion (No-Code ETL Platform) for Google Big Query
Chainsys
Data Ingestion (No-Code ETL Platform) for Google Big Query
Chainsys
Data Ingestion (No-Code ETL Platform) for Google Big Query
Chainsys
Migration, Integration Reference data from any data source or legacy system to Google Big Query
A Data Warehouse is only as good as the quality of the data stored within. To completely realize the benefits of Google BigQuery’s enterprise data warehouse, it must be ingested with trustworthy, relevant, and quality data in a timely manner. ChainSys’ customizable dataZap platform (iPaaS) can seamlessly access and load data into Google BigQuery from all types of data sources including, structured, unstructured, sensors, images, video, etc. Other data sources also include social media, machine sources, on-prem systems and relational databases. ChainSys’ products provide a simple but agile platform to kick start cloud analytics projects on BigQuery almost instantly and scale it as per volume of data and needs. This unparalleled mashup between ChainSys and Google BigQuery enhances your experience by accessing the true power in BigQuery. It expedited all workstreams from migration to analytics.
Key features include:
Drag-and-drop approach to visually design, develop, and deploy data integration mappings
Low Code / No Code focus
Automated scheduling of workflows and jobs
10000+ Pre-Built Templates
Streaming makes data available immediately for querying and analysis in real time.
Bulk data integration is provisioned to optimize large data volumes
Automated parallel loading into BigQuery using Google Cloud Storage for optimizing BigQuery processing power
Read from and write to Google BigQuery data sets
Robust security
Key Benefits:
Rapid Data Transformation for Analytics
ChainSys is quick to clean and make data available for results. Adding this to BigQuery's ability to process multi-terabyte queries in seconds, and analysis can be done in almost real time.
Analyze, format, and aggregate records rapidly
For quality results and analytics, quality of data needs to be maintained. While BigQuery allows anyone in the organization with SQL Knowledge to query immense data loads, ChainSys platforms with their low-code / no-code approach makes this data more accessible and therefore, better insights can be harnessed.