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Data Engineering Annotated Monthly – May 2022

Big Data Tools

DataHub 0.8.36 – Metadata management is a big and complicated topic. On top of that, it’s a part of the Hadoop platform, which created additional work that we otherwise would not have had to do. DataHub is a completely independent product by LinkedIn, and the folks there definitely know what metadata is and how important it is.

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Data Engineering Annotated Monthly – May 2022

Big Data Tools

DataHub 0.8.36 – Metadata management is a big and complicated topic. On top of that, it’s a part of the Hadoop platform, which created additional work that we otherwise would not have had to do. DataHub is a completely independent product by LinkedIn, and the folks there definitely know what metadata is and how important it is.

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97 things every data engineer should know

Grouparoo

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

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HDFS Data Encryption at Rest on Cloudera Data Platform

Cloudera

To prevent the management of these keys (which can run in the millions) from becoming a performance bottleneck, the encryption key itself is stored in the file metadata. Each file will have an EDEK which is stored in the file’s metadata. hdfs dfs -cat” on the file triggers a hadoop KMS API call to validate the “DECRYPT” access.

MySQL 70
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Open-Sourcing AvroTensorDataset: A Performant TensorFlow Dataset For Processing Avro Data

LinkedIn Engineering

An Avro file is formatted with the following bytes: Figure 1: Avro file and data block byte layout The Avro file consists of four “magic” bytes, file metadata (including a schema, which all objects in this file must conform to), a 16-byte file-specific sync marker, and a sequence of data blocks separated by the file’s sync marker.

Datasets 102
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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 ?

Bytes 72
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Kafka Connect Deep Dive – Error Handling and Dead Letter Queues

Confluent

Headers are additional metadata stored with the Kafka message’s key, value and timestamp, and were introduced in Kafka 0.11 (see KIP-82 ). f 'nKey (%K bytes): %k Value (%S bytes): %s Timestamp: %T Partition: %p Offset: %o Headers: %hn'. To fix the pipeline, we need to resolve the issue with the message on the source topic.

Kafka 111