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Upload Data to Snowflake with Pre-defined Processors for Free

Moving data from storage or directories to Snowflake or any other destination? Standardize, normalize, and load file data automatically with over 60 pre-built processors.

Data Engineer’s Handbook: 4 Cloud Design Patterns for Data Ingestion and Transformation

Our customers run millions of data pipelines using StreamSets

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Securely Automate File and Data Loading Into Snowflake

Take Action on Data Discrepancies for More Accurate Loading

Datasets almost always have discrepancies that can be easily fixed record by record. Efficient data engineers proactively take action on these smaller issues so once the data lands into Snowflake it can be moved through the curation processes faster with greater accuracy. Finally, less break-fix time and more accurate data available for immediate analysis.

Handle Data Drift Automatically

Receiving files with little to no standardization and consistency in structure? It’s a common frustration for data engineers. You have great skills but are focusing on menial effort. Fix it and forget it with StreamSets. Detect and automatically handle data drift, the inevitable changes in schema, semantics, and infrastructure for both the source and destination. Finally, say goodbye to building pipelines with blindspots.

Easily Deploy with An Intuitive Drag and Drop Interface

Easily install StreamSets secure engines in your environment and securely move data into Snowflake. We both enforce the security that’s already defined in your existing environment and make sure your team has their required autonomy to securely deploy, design, and run pipelines. Without delay, Snowflake’s ready to do the heavy lifting.

More with 60 Pre-Built Data Processors to Copy Pristine Datasets into Snowflake on Autopilot

When new files land in places such as AWS S3 or your local directory behind your firewall, StreamSets goes to work, automatically reading the files and applying the processors to the data to ensure it’s complete, consistent and valid. Best of all, get automatically notified of processing errors when something falls beyond the boundaries you’ve set.

Field Remover to Eliminate Unnecessary Data

Got a file from your sales team in Germany with junky personal notes included? Or maybe a file with duplicate data? Remove a local teams’ “notes” field along with any other fields that are not needed for down-stream analytics. With Field Remover, easily select and keep the fields you want and discard those you don’t before copying into Snowflake.

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Upload Data To Snowflake
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Expression Evaluator to Normalize Fields or Perform Calculations

Our uniquely flexible expression evaluator can perform calculations and write the results to new or existing fields, add or modify record header attributes and field attributes, and more. Easily drop in custom Jython, Java or Groovy libraries to apply virtually any processing logic to your data.

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Stream Selector for Conditional Routing

Pulling in CSV data from several different customer reports in a single data pipeline? It’s unlikely you’ll need to process these files in the same way before uploading the data to Snowflake. Select and route records through your pipeline based on pre-set conditions with our stream selector.

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Stream Selector ETL
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Field Renamer to Rename Record Fields

Matching source and destination fields is a constant battle. Now, you could explicitly specify each field, but that’s a bit laborious, not to mention brittle in the face of data drift. Handle new fields appearing in the input data automatically. With Field Renamer, configure behavior when a source field does not exist, when a target field with a matching name already exists, when a source field matches multiple source field expressions, and more.

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Field Flattener to Ensure Files Meet Conditions Before Loading

Data formats such as Avro and JSON sometimes represent hierarchical structures, where records contain fields that are themselves a collection of fields, such as an address field. Many destinations such as Snowflake or your Delta Lake, however, require a ‘flat’ record, where each field is a single string, integer, etc. Use Field Flattener to flatten the structure of your entire record or just a specific field automatically before uploading data to Snowflake.

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StreamSets Does More Than Simplify Data Processing

StreamSets offers ease of use, reusability, cross platform support, and automatic drift detection

Embrace a Single Experience for All Patterns

Take advantage of a single, easy-to-use tool for all workloads (CDC, Streaming & Batch) that is proven to accelerate delivery efforts 10X. Finally, you can focus on higher order projects.

Avoid Breakages

StreamSets enables you to quickly build smart data pipelines that proactively address unforeseen pipeline changes. At last, dramatically reduce reactive break-fix and maintenance efforts.

Take Control of Your Environment

Last but not least, operate and monitor all data pipelines in one place for full visibility across all environments: on-premise/self managed, hybrid, cloud and multi-cloud environments.

What Our Customers Say

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“There is no data drift coming into play and I usually never get a surprise about how the data changes. StreamSets picks up the hiccups in the transactional system.”

Jeffrey Jennings, VP of Data, Acertus

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“With StreamSets, we’ve helped customers in multiple verticals to collect data from a wide variety of different sources outside of just databases, including data lakes, APIs, and sensors. The time between data collection and delivery reduces from overnight to near real-time while increased monitoring capabilities result in cost savings and resiliency.”

George Barrett, Solutions Engineer, SME

Upload data to Snowflake or any other destinationautomatically and securely.

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