Pipelines for data in motion on AWS

“We use Upsolver to ingest and optimize 20B events per day into our data lake on AWS, resulting in fresh data being available within minutes and a 10X acceleration of data lake queries.”

– Boaz Goldstein, R&D Manager, Data Architecture & Business Intelligence, Peer39.

Upsolver in the AWS ecosystem

Upsolver is a serverless data pipeline platform.  Its the fastest way to build pipelines that ingest and transform streaming and batch data for use in Athena, Redshift and other native and 3rd-party systems on AWS.

AWS Glue and AWS EMR are native services for building data processing pipelines on AWS.  However, these systems require expertise in technologies such as Python, Scala, Spark and Airflow. Upsolver users write pipelines with a guided wizard that uses SQL for declaring pipeline logic and blends batch and streaming data sources in a single pipeline, and it automatically orchestrates and optimize pipelines.

As we like to say, “Write a query, get a pipeline.”

Upsolver AWS Glue AWS EMR
Main dev language Wizard or SQL Python/Scala Python/Scala/Java
Orchestration Self-orchestrated Manual (+Airflow) Manual (+Airflow)
Stream+Batch 1 engine Separate engines Separate engines
Recommended for SQL data engineers
Non data engineers
Spark data engineers Spark data engineers

SQL simplicity combined with AWS scale

Upsolver combines the simplicity of wizard- or SQL-based pipeline development with the infinite operating scale of Amazon object storage and processing. With Upsolver, creating an always-on data pipeline that delivers up-to-the-minute, high-quality data from event streams, logs and database sources is as easy as filling out a form or writing a query. Upsolver automatically creates a data lake for raw data that is queryable and highly performant. At its core, Upsolver is a stream processing engine combined with a scalable state store for large joins, aggregations, and upserts.

Without Upsolver, data engineers need to stitch together a solution across multiple AWS services, including Glue streaming, Glue batch, Glue crawlers, step functions or Airflow for orchestration, Amazon DynamoDB for state management, and scripts for implementing best practices to optimize performance.

With Upsolver, you can:

  • Continuously deliver up-to-the-minute, transformed data from streams, files and databases to your data lake, data warehouse and other analytics systems.
  • Unify real-time streams and historical batch data in one platform.
  • Get to value faster by eliminating manual orchestration, optimization and infrastructure management.

Reference architecture for AWS

Ingestion: Upsolver connects data sources such as Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka (MSK), Amazon S3, Amazon Aurora, and Amazon RDS.

Outputs: Live tables are output to Amazon Redshift, Amazon S3, and Amazon Athena, as well as to 3rd-party services such as Snowflake and Elasticsearch.

Processing: Upsolver runs on AWS infrastructure and leverages Amazon S3 for affordable storage, Amazon EC2 Spot for low-cost data processing, and AWS Glue Data Catalog for metadata management.

SQLake Reference Architecture for AWS


AWS Foundational Technical Review, AWS Athena integrated partner, Amazon Redshift Ready partner, AWS Advanced Technology Partner: Data & Analytics Competency, Upsolver is certified as SOC 2 Type II compliant.

Some of the companies winning with Upsolver and AWS

ironSource The Meet Group similarweb Bigabid
How ironSource Built a  Data Lake with Upsolver

ironSource uses Upsolver to build, manage, and orchestrate its data lake with minimal coding and maintenance. They saved hundreds of thousands of dollars per year by creating an architecture that separates compute and storage.

The Meet Group drives real-time insights with Upsolver

The Meet Group is a leading provider of online dating solutions. After several acquisitions, The Meet Group sought a solution to integrate its data pipelines and central data collection to drive better real-time analysis.

SimilarWeb Analyzes Hundreds of Terabytes

SimilarWeb reduced time to insight from 24 hours to minutes with a performant, cost effective, and efficient solution built on Athena for SQL analytics, S3 for events storage, and Upsolver for data pipelines.

Bigabid Improves its Modeling Accuracy 200x

Bigabid drives new user insights and advertising opportunities with machine learning via Upsolver and AWS. Using Upsolver’s data pipeline platform, Bigabid built a working proof of concept for its real-time pipeline in hours.

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