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Manufacturing Data Ingestion into Snowflake

Snowflake

Accessing data from the manufacturing shop floor is one of the key topics of interest with the majority of cloud platform vendors due to the pace of Industry 4.0 Working with our partners, this architecture includes MQTT-based data ingestion into Snowflake. Industry 4.0, Stay tuned for more insights on Industry 4.0

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Improved Ascend for Databricks, New Lineage Visualization, and Better Incremental Data Ingestion

Ascend.io

More and more customers are dramatically accelerating their time to value with Databricks data pipelines by leveraging Ascend automation. Instead, it is a Sankey diagram driven by the same dynamic metadata that runs the Ascend control plane. Improved performance by upgrading our ingestion engine from Spark 3.2.0

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Data Engineering Zoomcamp – Data Ingestion (Week 2)

Hepta Analytics

DE Zoomcamp 2.2.1 – Introduction to Workflow Orchestration Following last weeks blog , we move to data ingestion. We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design.

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The Data Integration Solution Checklist: Top 10 Considerations

Precisely

A true enterprise-grade integration solution calls for source and target connectors that can accommodate: VSAM files COBOL copybooks open standards like JSON modern platforms like Amazon Web Services ( AWS ), Confluent , Databricks , or Snowflake Questions to ask each vendor: Which enterprise data sources and targets do you support?

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Data Engineering Weekly #164

Data Engineering Weekly

The APIs support emitting unstructured log lines and typed metadata key-value pairs (per line). Ingestion clusters read objects from queues and support additional parsing based on user-defined regex extraction rules. The extracted key-value pairs are written to the line’s metadata.

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5 Layers of Data Lakehouse Architecture Explained

Monte Carlo

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Ingestion layer 2. Metadata layer 4. API layer 5.

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Data Lakehouse Architecture Explained: 5 Layers

Monte Carlo

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1. Ingestion layer 2. Metadata layer 4. API layer 5.