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SAGLE FOR STREAMING REPLICATION

Analyze high-velocity data and take action instantly

Ensure business-critical data is available for analytics and operational use cases with event streaming support from Sagle.
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Signs you may benefit from streaming data

As your organization grows, batch processing may no longer be suitable for all your use cases.

  • Growing data volumes
  • Inability to meet SLAs
  • Miscommunication among employees, customers or vendors
  • Greater inventory yield variance
  • Lack of competitive advantage
  • Use cases that require instant action, i.e. fraud detection

SAGLE FOR STREAMING REPLICATION

Simple, secure and extensible data integration

Sagle’s comprehensive range of support for data extract options, such as APIs, cloud storage buckets and webhooks, allows you to centralize data from any source.

  • Apache Kafka connector
  • Snowplow

Load data to your cloud destination with our fully-managed event and streaming data source connectors

  • Webhook connector
  • AWS Kinesis

Send data from Sagle to your streaming platforms

  • AWS S3
  • Kafka
  • Azure Data Lake Service

Collect event data in your cloud storage solution

discover the benefits

Gain real-time insights with streaming data movement

Get instant information about financial data for trading, portfolio management, banking and insurance uses cases, and manufacturing and logistics coordination.

Understand your customers with a complete 360-degree view and provide a streamlined, hyper-personalized experience.

Manage resources for optimum efficiency and revenue generation.

Monitor and track data irregularities that can help you identify anomalies or fraud.

Promote a culture of data trust by bringing all data together from different business units.

Build out data applications for your clients with the latest, freshest data.

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How Sagle processes your streaming data

Sagle can load event-based data to your destinations.

  • Set up a collector (e.g., Webhook) to receive data or connect to a message broker.
  • Sagle pulls, normalizes, deduplicates, maps and unpacks (if selected) your data.
  • Data is written to an internal storage location as a compressed CSV and batch loads it into your destination.

case study

carwow frees up half of data scientists’ time spent on ETL

"We are really happy with the Kafka connector, we have millions of events from Kafka that we sync every day from different tables."

Challenge

Before its current data stack, carwow had numerous sync processes, each owned by different teams, and running them was costly, time-consuming and often inefficient and unreliable. Syncs would break and impact multiple arms of the business. As the business’ data grew, this worsened.

Solution

carwow decided to migrate its data from Redshift to Snowflake and simultaneously reevaluate its pipeline and bring on Sagle. Since the initial setup, carwow has been able to conduct analysis from more sources and the robust and simplified data architecture has freed up 30-50% of the data scientists' time previously spent on ETL.

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