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Announcing New Innovations for Data Warehouse, Data Lake, and Data Lakehouse in the Data Cloud 

Snowflake

These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh. We’re committed to giving customers a choice and the ability to adapt while maintaining our core tenets of strong security and governance, excellent performance and simplicity.

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Data Engineering Weekly #145

Data Engineering Weekly

I often want to click the “Schedule this Notebook” button and automatically generate the Airflow code to schedule and commit in Github. link] Kyle Weller: Delta, Hudi, Iceberg — A Benchmark Compilation The LakeHouse architecture brings the best of the database and the data lake into the data infrastructure.

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Taking Charge of Tables: Introducing OpenHouse for Big Data Management

LinkedIn Engineering

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.

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What is ETL Pipeline? Process, Considerations, and Examples

ProjectPro

Table of Contents What is ETL Pipeline? Historically, data extraction meant retrieving information from files like Excel, CSV, Text, etc. Storing data in raw format is still prevalent, as they were the primary sources of customer information. Basic Cleaning: Converting data into a suitable format as per our requirement.

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DataOps: What Is It, Core Principles, and Tools For Implementation

phData: Data Engineering

You can read the full guide without giving us your email — keep scrolling !) Table of Contents How Impactful is Your Data? Most companies begin by using Microsoft Excel , downloading CSV files from a variety of sources in order to clean data, perform analytics, and generate reports. Want to Save This eBook for Later?

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