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Testing and Monitoring Data Pipelines: Part One

Dataversity

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

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Top 20 Data Warehouse Best Practices in 2024

Astera

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Data Pine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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Information Marts: Enabling Agile, Scalable, and Accurate BI

Astera

Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. The combination of data vault and information marts solves this problem.

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ETL Using Python: Exploring the Pros vs. Cons

Astera

There are several ETL tools written in Python that leverage Python libraries for extracting, loading and transforming diverse data tables imported from multiple data sources into data warehouses. Supports multiple data types and formats but requires additional libraries for different sources.

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The Complete Guide to Reverse ETL

Astera

Reverse ETL (Extract, Transform, Load) is the process of moving data from central data warehouse to operational and analytic tools. How Does Reverse ETL Fit in Your Data Infrastructure Reverse ETL helps bridge the gap between central data warehouse and operational applications and systems.

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Overcoming Snowflake Challenges – A Practical Guide 

Astera

That’s how it can feel when trying to grapple with the complexity of managing data on the cloud-native Snowflake platform. They range from managing data quality and ensuring data security to managing costs, improving performance, and ensuring the platform can meet future needs. So, let’s get started!