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Hands-On Introduction to Delta Lake with (py)Spark

Towards Data Science

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture.

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A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. Understanding the essential components of data pipelines is crucial for designing efficient and effective data architectures.

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Changing face of real-time analytics

Rockset

This means new data schemas, new sources and new types of queries pop up every few days. When you are evaluating your real-time analytics solutions, look at not just price-performance but also flexibility to handle new data formats and new types of queries so that you are future-roadmap-proof.

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Data Mesh Architecture: Revolutionizing Event Streaming with Striim

Striim

Marketing teams should have easy access to the analytical data they need for campaigns. Furthermore, the self-serve data infrastructure should include encryption, data product versioning, data schema, and automation.

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What is Data Engineering? Skills, Tools, and Certifications

Cloud Academy

Part of the Data Engineer’s role is to figure out how to best present huge amounts of different data sets in a way that an analyst, scientist, or product manager can analyze. What does a data engineer do? A data engineer is an engineer who creates solutions from raw data.

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Snowflake Observability and 4 Reasons Data Teams Should Invest In It

Monte Carlo

Optimizing Snowflake migration and management We’ve previously covered how data observability solutions can help you migrate to Snowflake like a boss , but to summarize: When moving from a partition/index to cluster model be sure to document and analyze current data schema and lineage to select appropriate cluster keys as needed.

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The JaffleGaggle Story: Data Modeling for a Customer 360 View

dbt Developer Hub

I intentionally left out two seed files, one of which data/merged_user.csv contains users the JaffleGaggle team have identified as the same person. Oftentimes, in a CRM’s data schema, there’s a built-in treatment for handling merged entities.