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Data News — Week 22.45

Christophe Blefari

Modeling is often lead by the dimensional modeling but you can also do 3NF or data vault. When it comes to storage it's mainly a row-based vs. a column-based discussion, which in the end will impact how the engine will process data. The end-game dataset. This is probably the concept I liked the most from the video.

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The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring

DataKitchen

This blog post explores the challenges and solutions associated with data ingestion monitoring, focusing on the unique capabilities of DataKitchen’s Open Source Data Observability software. This process is critical as it ensures data quality from the onset. Have all the source files/data arrived on time?

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Apache Spark MLlib vs Scikit-learn: Building Machine Learning Pipelines

Towards Data Science

Although within a big data context, Apache Spark’s MLLib tends to overperform scikit-learn due to its fit for distributed computation, as it is designed to run on Spark. Datasets containing attributes of Airbnb listings in 10 European cities ¹ will be used to create the same Pipeline in scikit-learn and MLLib. Source: The author.

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Data Warehouse vs Big Data

Knowledge Hut

In the modern data-driven landscape, organizations continuously explore avenues to derive meaningful insights from the immense volume of information available. Two popular approaches that have emerged in recent years are data warehouse and big data. Big data offers several advantages.

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Modern Data Engineering

Towards Data Science

Indeed, datalakes can store all types of data including unstructured ones and we still need to be able to analyse these datasets. What I like about it is that it makes it really easy to work with various data file formats, i.e. SQL, XML, XLS, CSV and JSON. You can change these # to conform to your data. Datalake example.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

You can produce code, discover the data schema, and modify it. Smooth Integration with other AWS tools AWS Glue is relatively simple to integrate with data sources and targets like Amazon Kinesis, Amazon Redshift, Amazon S3, and Amazon MSK. For analyzing huge datasets, they want to employ familiar Python primitive types.

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Large-scale User Sequences at Pinterest

Pinterest Engineering

We set up a separate dataset for each event type indexed by our system, because we want to have the flexibility to scale these datasets independently. In particular, we wanted our KV store datasets to have the following properties: Allows inserts. We need each dataset to store the last N events for a user.