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Best Practices for Data Ingestion with Snowflake: Part 3 

Snowflake

Welcome to the third blog post in our series highlighting Snowflake’s data ingestion capabilities, covering the latest on Snowpipe Streaming (currently in public preview) and how streaming ingestion can accelerate data engineering on Snowflake. What is Snowpipe Streaming?

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Comparing Snowflake Data Ingestion Methods with Striim

Striim

Introduction In the fast-evolving world of data integration, Striim’s collaboration with Snowflake stands as a beacon of innovation and efficiency. Snowpipe Streaming: Unleashing Real-Time Data Integration and AI Snowpipe Streaming, when teamed up with Striim, is kind of like a superhero for real-time data needs.

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What is Data Ingestion? Types, Frameworks, Tools, Use Cases

Knowledge Hut

An end-to-end Data Science pipeline starts from business discussion to delivering the product to the customers. One of the key components of this pipeline is Data ingestion. It helps in integrating data from multiple sources such as IoT, SaaS, on-premises, etc., What is Data Ingestion?

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Stream Rows and Kafka Topics Directly into Snowflake with Snowpipe Streaming

Snowflake

Now we are able to ingest our data in near real time directly from Kafka topics to a Snowflake table, drastically reducing the cost of ingestion and improving our SLA from 15 minutes to within 60 seconds. Streaming data and historical data should not live in silos or cause infrastructure management complexity.

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Going From Transactional To Analytical And Self-managed To Cloud On One Database With MariaDB

Data Engineering Podcast

Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.

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Updates, Inserts, Deletes: Comparing Elasticsearch and Rockset for Real-Time Data Ingest

Rockset

Elasticsearch was designed for log analytics where data is not frequently changing, posing additional challenges when dealing with transactional data. Rockset, on the other hand, is a cloud-native database, removing a lot of the tooling and overhead required to get data into the system.

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Tame The Entropy In Your Data Stack And Prevent Failures With Sifflet

Data Engineering Podcast

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

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