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How to Ensure Data Integrity at Scale By Harnessing Data Pipelines

Ascend.io

Foundational encoding, whether it is ASCII or another byte-level code, is delimited correctly into fields or columns and packaged correctly into JSON, parquet, or other file system. It should detect “schema drift,” and may involve operations that validate datasets against source system metadata, for example. In a valid schema.

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Open-Sourcing AvroTensorDataset: A Performant TensorFlow Dataset For Processing Avro Data

LinkedIn Engineering

To remove this bottleneck, we built AvroTensorDataset , a TensorFlow dataset for reading, parsing, and processing Avro data. Today, we’re excited to open source this tool so that other Avro and Tensorflow users can use this dataset in their machine learning pipelines to get a large performance boost to their training workloads.

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Aligning Velox and Apache Arrow: Towards composable data management

Engineering at Meta

Why we need a composable data management system Meta’s data engines support large-scale workloads that include processing large datasets offline (ETL), interactive dashboard generation, ad hoc data exploration, and stream processing. In the new representation , the first four bytes of the view object always contain the string size.

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A Definitive Guide to Using BigQuery Efficiently

Towards Data Science

Like a dragon guarding its treasure, each byte stored and each query executed demands its share of gold coins. Join as we journey through the depths of cost optimization, where every byte is a precious coin. It is also possible to set a maximum for the bytes billed for your query. Photo by Konstantin Evdokimov on Unsplash ?

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How Netflix microservices tackle dataset pub-sub

Netflix Tech

By Ammar Khaku Introduction In a microservice architecture such as Netflix’s, propagating datasets from a single source to multiple downstream destinations can be challenging. One example displaying the need for dataset propagation: at any given time Netflix runs a very large number of A/B tests.

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Data Vault Architecture, Data Quality Challenges, And How To Solve Them

Monte Carlo

such as its suitability for auditing, quickly redefining relationships, and easily adding new datasets. architecture (with some minor deviations) to achieve their data integration objectives around scalability and use of metadata. “A Pie Insurance , a leading small business insurtech, leverages a data vault 2.0

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Monte Carlo + Databricks Doubles Mutual Customer Count—and We’re Just Getting Started

Monte Carlo

Data lakes often contain larger datasets than what you’d find in a warehouse, including massive amounts of unstructured data that wouldn’t be possible in a warehouse environment. The Unity Catalog unifies metastores, catalogs, and metadata within Databricks.