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Go vs. Python for Modern Data Workflows: Need Help Deciding?

KDnuggets

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Go vs. Python for Modern Data Workflows: Need Help Deciding?

Python 286
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Data lakes vs. data warehouses: Decoding the data storage debate

Data Science Dojo

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

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What Is a Lakebase?

databricks

Separation of storage and compute : Lakebases store their data in modern data lakes (object stores) in open formats, which enables scaling compute and storage separately, leading to lower TCO and eliminating lock-in. At zero, the cost of the lakebase is just the cost of storing the data on cheap data lakes.

Database 215
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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. It offers full BI-Stack Automation, from source to data warehouse through to frontend.

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Data lakehouse

Dataconomy

Data Lakehouse has emerged as a significant innovation in data management architecture, bridging the advantages of both data lakes and data warehouses. By enabling organizations to efficiently store various data types and perform analytics, it addresses many challenges faced in traditional data ecosystems.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools.

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The IKEA of Data: How to Bring Modular Thinking to Your Data Architecture (and Why It Works)

IBM Data Science in Practice

The Data Dilemma: From Chaos toClarity In the world of data management, weve all beenthere: A simple request spirals into a maze of thingslike: A CSV labeled final_v2_final_final.csv A Parquet file in a forgotten S3folder A table with brokenlineage An abandoned SQL notebook from yearsago What starts as a data lake often becomes a dataswamp!