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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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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. The Lakehouse architecture was one of them.

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Snowflake Observability and 4 Reasons Data Teams Should Invest In It

Monte Carlo

Adopting a cloud data warehouse like Snowflake is an important investment for any organization that wants to get the most value out of their data. Most data teams, especially those early in their Snowflake journey, have yet to fully unlock full potential and value from this key investment. as well as reliability.

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17 Super Valuable Automated Data Lineage Use Cases With Examples

Monte Carlo

Solutions with automated data lineage capabilities constantly update these graphs and illustrate them as nodes and edges, or in other words, the objects through which the data travels and the relationship between them. This is one of the most frequent data lineage use cases leveraged by Vox. Data lineage can help!

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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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The JaffleGaggle Story: Data Modeling for a Customer 360 View

dbt Developer Hub

This aggregation process requires an analytics warehouse, as all of these things need to be synced together outside of the application database itself to incorporate other data sources (billing / events information, past touchpoints in the CRM, etc). You most definitely deserve a jaffle. ?

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100+ Big Data Interview Questions and Answers 2023

ProjectPro

Step 5: Data Validation This is the last step involved in the process of data preparation. In this step, automated procedures are used for the data to verify its accuracy, consistency, and completeness. The prepared data is then stored in a data warehouse or a similar repository.