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Move Data From Postgres to Snowflake
Quickly and easily get your data moving from Postgres to Snowflake with no infrastructure to manage. Our fully-managed data movement platform provides a streamlined and cost-effective connectivity, transport, and processing solution to save you time, money, and effort.
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Used by the world's fasted growing companies
You Are Just Minutes Away From Analyzing Your Postgres Data With Snowflake in Real Time
Step 1
Choose A Source
Use Decodable’s pre-built connectors to connect to your source systems. You control the scale of the connection by specifying the task size and count.
Step 2
Choose A Destination
Efficiently stream data into Snowflake or one of dozens of other destinations supported by Decodable.
Step 3
Move Your Data
You can select specific tables and fields in Postgres, manage the schema, make optional real-time transformations, and get exactly what you need into Snowflake.
What can I do with Decodable?
Replicate data from operational databases to your data warehouse in real-time using Debezium-powered change data capture connectors. Low-overhead, high-throughput, continuous data means the analytics and modeling on the freshest data available.
Capture clickstream, orders, inventory updates, product telemetry, and other application events from streaming systems like Apache Kafka. Cleanse and transform event data and ingest it into your data lakes so it's optimized and ready for analytics. Handle everything from data quality to format conversion and partitioning in real-time.
Pipelines can be arranged in a directed acyclic graph (DAG) just as you wound in batch data processing. Separate complex jobs into easy to manage units without losing efficiency. Allow downstream teams and processes to further refine data the way they need to without impacting upstream workloads.
Pipelines can be arranged in a directed acyclic graph (DAG) just as you wound in batch data processing. Separate complex jobs into easy to manage units without losing efficiency. Allow downstream teams and processes to further refine data the way they need to without impacting upstream workloads.
The Fastest Way To Replicate Your Data From Postgres To Snowflake
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Why Choose Decodable As Your Data Movement Platform?
Flexible and Scalable
Scale up as needed to handle the most demanding workloads. Only pay for what you use.
SQL or Code
Use SQL to process data including support for joins, CTEs, and CEP. Or create more advanced processing using Apache Flink APIs.
Hosted or Your Cloud
No need to provision, configure or manage any infrastructure, we’ll host the entire system for you. Prefer to leverage your cloud? Your data can stay put and we’ll come to you.
Works Your Way
Use the Decodable UI, CLI, APIs, or even dbt to set up and run pipelines.
SOC2 Type II - GDPR Compliant
Compliant with critical regulatory requirements including SOC2 Type II and GDPR so you know your data is safe.
Secure
Integrated with your identity provider for SSO. Flexible RBAC to secure access to your data and infrastructure.
Integrated
All of the power and flexibility of industry-standard open source, pre-integrated and ready to go.
Pre-Built Connector Library
Take advantage of a large library of connectors - including DBZ -based CDC connectors, to ingest data from any source, and send data to any sink with minimal configuration.
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Start Using Decodable
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Frequently Asked Questions
We define data movement to include connectivity, transport, and optional processing. It is a superset of ETL, ELT, and stream processing.
Extract, Transform, and Load is a common approach to data movement and integration. Data is extracted from source systems, transformed into the desired format, and loaded into a destination database, data lake, or data warehouse. This approach is intended to optimize getting the right data, in the right form, into systems for data analysis.
Extract, Load, and Transform is a variant of data movement where the data is first extracted from source systems, loaded into a data lake or warehouse, and then transformed by the destination system. This approach is intended to optimize data ingestion.
Stream processing, or event stream processing, is a data movement process that involves ingesting a continuous data stream, as opposed to processing data in batches. It is optimized to quickly move, filter, route, normalize, or otherwise transform data in real time. As data is processed, it moves on to a data warehouse, application, or additional stream processing steps. Stream processing services are growing in popularity due to the new and valuable use cases they enable.
PostgreSQL is a powerful, open source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads.
Snowflake is a fully managed software-as-a-service (SaaS) that provides a single platform for data warehousing, data lakes, data engineering, data science, data application development, and secure sharing and consumption of real-time and shared data.
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