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USE CASE | SHIFT-LEFT ANALYTICS
シフトレフトで実現
|データ作成から数ミリ秒以内にソースでデータを処理・管理することで、無駄なデータの拡散、手作業による修正と高コストを排除します。
Why Shift Processing & Governance Left?
In data integration, “shifting left” refers to an approach where data processing and governance are performed closer to the source of data generation. By cleaning and processing the data earlier in the data lifecycle, you can build data products that give all downstream consumers—including cloud data warehouses, data lakes, and data lakehouses—a single source of well-defined and well-formatted data.
“Confluent helped us shift left on our data—giving teams full ownership of their data from source to output. Now it’s clean, validated, and production-ready upstream, reducing rework and accelerating delivery."
"Confluent Tableflow simplifies our data architecture, turning Kafka topics into trusted, analytics-ready tables in just a few clicks. We can now ensure a reliable data foundation for real-time customer insights and AI innovations."
Zazzle implemented Confluentʼs fully-managed Flink offering to transform their largest data pipeline. By shifting stream processing earlier in the pipeline before writing to Google BigQuery, Zazzle reduced storage and computation costs while delivering more relevant product recommendations, directly impacting revenue.
“We have 40,000+ ETL jobs today. It’s chaos…shifting data governance left would be transformational for our organization.”
“[Data cleaning] is a pricey way of pushing it down to the Delta Lake. De-duplication within Confluent is a cheaper way of doing it. We can only do it once.”
“I love the vision of [shift-left analytics]. This is how we would make datasets more discoverable. I knew that Confluent had an integration with Alation but it's awesome to hear that [with Data Portal] you have other ways of enabling those capabilities”
トレンド
データ格差の解消 : Confluent と Databricks がリアルタイム AI を解き放つ方法
Confluent と Databricks の緊密な統合により、AI 対応のデータプロダクトを全社的に実現する方法をご覧ください。
Show Me How: Shift Left Processing From Data Warehouses
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How to Optimize Data Ingestion Into Lakehouses & Warehouses
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Tableflow Is GA: Unifying Apache Kafka® Topics & Apache Iceberg™️ Tables
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Conquer Your Data Mess With Universal Data Products
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革新的な組織が Confluent を使用してデータ処理とガバナンスをどのようにシフトレフトしているかをご覧ください。
Maximize Your Data Warehouses in 4 Steps
Maximize your data warehouses and data lakehouses by feeding them fresh trustworthy data. It all starts when you have a complete data streaming platform that lets you stream, connect, govern, and process your data (and materialize it in open table formats) no matter where it lives.
Step 3. Enable Self-Service Access to Trustworthy Data Products
- Ensure data quality at the source: Build trusted, high quality data products with explicit data contracts and schema management with Stream Governance.
- Discover reusable data products: Help data consumers securely self-serve to search and repurpose data products via Data Portal.
- Quickly understand complex data relationships: Gain more insights with an interactive, visual overview of data flows and processing with Stream Lineage.
Learn how Confluent can help your organization shift left and maximize the value of your data warehouse and data lake workloads. Connect with us today to learn how to adopt shift-left architectures and accelerate your analytics and AI use cases.