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Data News — Week 23.08

Christophe Blefari

Data engineering team moving data manually ( credits ) Dear readers, I hope you had a great week. This is something I struggle with, I really like writing, I really like this newsletter, I really like the blog, but it takes me one day per week to be done. For the last 2 newsletters I've tried to ask your for paying support.

Kafka 130
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How to Use DBT to Get Actionable Insights from Data?

Workfall

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights. These guards are tests in DBT.

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15+ Best Data Engineering Tools to Explore in 2023

Knowledge Hut

The tremendous growth in data generation, then the rise in data engineer jobs - there’s no arguing the fact that the big data industry is at its best pace and you, as an aspiring data engineer, have a lot to learn and make out of it - including some tools! What are Data Engineering Tools?

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Accelerating ML Training And Delivery With In-Database Machine Learning

Data Engineering Podcast

Summary When you build a machine learning model, the first step is always to load your data. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. RudderStack’s smart customer data pipeline is warehouse-first.

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Real World Change Data Capture At Datacoral

Data Engineering Podcast

For analytical systems, the only way to provide this reliably is by implementing change data capture (CDC). Unfortunately, this is a non-trivial undertaking, particularly for teams that don’t have extensive experience working with streaming data and complex distributed systems.

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Moving Enterprise Data From Anywhere to Any System Made Easy

Cloudera

Since 2015, the Cloudera DataFlow team has been helping the largest enterprise organizations in the world adopt Apache NiFi as their enterprise standard data movement tool. This need has generated a market opportunity for a universal data distribution service. Why does every organization need it when using a modern data stack?

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Optimizing Materialized Views with dbt

dbt Developer Hub

on these following adapters: dbt-postgres dbt-redshift dbt-snowflake dbt-databricks dbt-materialize* dbt-trino* dbt-bigquery** *These adapters have supported materialized views in their adapter prior 1.6. **dbt-bigquery dbt-bigquery support will be coming in 1.7. At release, python models will not be supported.