My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself, which reduces the times for maintaining pipelines by up to 50%.
My main use case for Upsolver is during an IT consulting project for a large enterprise running a cloud-native data platform on AWS. I used Upsolver to ingest and process high-volume stream data from web, mobile, and microservices sources from Amazon Kinesis with semi-structured JSON and frequent schema changes. The goal was to deliver near-real-time analytics on S3 and Redshift while reducing the complexity and fragility of existing custom Spark pipelines. A specific example of how I used Upsolver in that project is that it handled the schema changes seamlessly. A new or modified JSON field did not break pipelines, which significantly improved stability in an agile environment. I used Upsolver for automatic schema evolution, and it was very useful for us.
I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs. We primarily serve financial service customers in India and around the globe. We use Upsolver as an ETL tool to move data from different sources into one destination quickly and at scale.
When I test-drove Upsolver for a consulting company, I used it in POC to stream and ingest data. The goal was to move data from a source, possibly SQL Server, into a destination like Snowflake or Redshift. The POC aimed to evaluate Upsolver against StreamSets, the competition for ETL tasks. The use case involved data aggregation, ingestion rules, landing data into a data lake, and handling ETL processes for a data warehouse.
Data Integration is a process that connects disparate data sources to streamline data management, enhance accuracy, and enable comprehensive analysis within an organization.This practice is critical for businesses aiming to consolidate data from diverse systems and databases. By doing so, organizations can ensure data consistency and integrity across all departments, supporting informed decision-making and operational efficiency. It also plays a significant role in enabling real-time...
My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself, which reduces the times for maintaining pipelines by up to 50%.
My main use case for Upsolver is during an IT consulting project for a large enterprise running a cloud-native data platform on AWS. I used Upsolver to ingest and process high-volume stream data from web, mobile, and microservices sources from Amazon Kinesis with semi-structured JSON and frequent schema changes. The goal was to deliver near-real-time analytics on S3 and Redshift while reducing the complexity and fragility of existing custom Spark pipelines. A specific example of how I used Upsolver in that project is that it handled the schema changes seamlessly. A new or modified JSON field did not break pipelines, which significantly improved stability in an agile environment. I used Upsolver for automatic schema evolution, and it was very useful for us.
I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs. We primarily serve financial service customers in India and around the globe. We use Upsolver as an ETL tool to move data from different sources into one destination quickly and at scale.
When I test-drove Upsolver for a consulting company, I used it in POC to stream and ingest data. The goal was to move data from a source, possibly SQL Server, into a destination like Snowflake or Redshift. The POC aimed to evaluate Upsolver against StreamSets, the competition for ETL tasks. The use case involved data aggregation, ingestion rules, landing data into a data lake, and handling ETL processes for a data warehouse.