Data Engineering

La donnée est l’énergie qui alimente la transformation digitale. Les développeurs la consomme dans leurs applicatifs. Les Data Analysts la fouille, la requête et la partage. Les Data Scientists alimentent leurs algorithmes avec. Les Data Engineers ont la responsabilité de mettre en place la chaîne de valeur qui inclue la collecte, le nettoyage, l’enrichissement et la mise à disposition des données.

Gérer le passage à l’échelle, garantir la sécurité et l’intégrité des données, être tolérant aux pannes, manipuler des données par lots ou en flux continu, valider les schémas, publier les APIs, sélectionner les formats, modèles et bases de données appropriés à leurs expositions sont autant de prérogatives à la charge du Data Engineer. De son travail découle la confiance et les succès de ceux qui consomme et exploitent la donnée.

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Articles associés au Data Engineering

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Comparison of database architectures: data warehouse, data lake and data lakehouse

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Catégories : Big Data, Data Engineering | Tags : Data Governance, Infrastructure, Iceberg, Parquet, Spark, Data Lake, Lakehouse, Data Warehouse, File Format

Database architectures have experienced constant innovation, evolving with the appearence of new use cases, technical constraints, and requirements. From the three database structures we are comparing…

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Databricks logs collection with Azure Monitor at a Workspace Scale

Databricks logs collection with Azure Monitor at a Workspace Scale

Catégories : Cloud Computing, Data Engineering, Adaltas Summit 2021 | Tags : Metrics, Monitoring, Spark, Azure, Databricks, Log4j

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An overview of Cloudera Data Platform (CDP)

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Catégories : Big Data, Cloud Computing, Data Engineering | Tags : SDX, Big Data, Cloud, Cloudera, CDP, CDH, Data Analytics, Data Hub, Data Lake, Lakehouse, Data Warehouse

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Find your way into data related Microsoft Azure certifications

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Storage size and generation time in popular file formats

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Catégories : Data Engineering, Data Science | Tags : Avro, HDFS, Hive, ORC, Parquet, Big Data, Data Lake, File Format, JavaScript Object Notation (JSON)

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Catégories : Big Data, Data Engineering | Tags : Business intelligence, Data structures, Avro, HDFS, ORC, Parquet, Batch processing, Big Data, CSV, JavaScript Object Notation (JSON), Kubernetes, Protocol Buffers

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Importing data to Databricks: external tables and Delta Lake

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Catégories : Data Engineering, Learning | Tags : Tuning, Hadoop, Spark, Python

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MLflow tutorial: an open source Machine Learning (ML) platform

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Catégories : Data Engineering, Data Science, Learning | Tags : AWS, Azure, Databricks, Deep Learning, Deployment, Machine Learning, MLflow, MLOps, Python, Scikit-learn

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Logstash pipelines remote configuration and self-indexing

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Catégories : Data Engineering, Infrastructure | Tags : Docker, Elasticsearch, Kibana, Logstash, Log4j

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Internship Data Science & Data Engineer - ML in production and streaming data ingestion

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Machine Learning model deployment

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Catégories : Big Data, Data Engineering, Data Science, DevOps & SRE | Tags : DevOps, Operation, AI, Cloud, Machine Learning, MLOps, On-premises, Schema

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Spark Streaming part 4: clustering with Spark MLlib

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Whenever services are unavailable, businesses experience large financial losses. Spark Streaming applications can break, like any other software application. A streaming application operates on data…

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Spark Streaming part 2: run Spark Structured Streaming pipelines in Hadoop

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Catégories : Data Engineering, Learning | Tags : Apache Spark Streaming, Spark, Python, Streaming

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Spark Streaming part 1: build data pipelines with Spark Structured Streaming

Spark Streaming part 1: build data pipelines with Spark Structured Streaming

Catégories : Data Engineering, Learning | Tags : Apache Spark Streaming, Kafka, Spark, Big Data, Streaming

Spark Structured Streaming is a new engine introduced with Apache Spark 2 used for processing streaming data. It is built on top of the existing Spark SQL engine and the Spark DataFrame. The…

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Publish Spark SQL DataFrame and RDD with Spark Thrift Server

Publish Spark SQL DataFrame and RDD with Spark Thrift Server

Catégories : Data Engineering | Tags : Thrift, JDBC, Hadoop, Hive, Spark, SQL

The distributed and in-memory nature of the Spark engine makes it an excellent candidate to expose data to clients which expect low latencies. Dashboards, notebooks, BI studios, KPIs-based reports…

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Par Oskar RYNKIEWICZ

25 mars 2019

Apache Flink: past, present and future

Apache Flink: past, present and future

Catégories : Data Engineering | Tags : Flink, Pipeline, Kubernetes, Machine Learning, SQL, Streaming

Apache Flink is a little gem which deserves a lot more attention. Let’s dive into Flink’s past, its current state and the future it is heading to by following the keynotes and presentations at Flink…

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Par César BEREZOWSKI

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Data Lake ingestion best practices

Data Lake ingestion best practices

Catégories : Big Data, Data Engineering | Tags : NiFi, Data Governance, HDF, Operation, Avro, Hive, ORC, Spark, Data Lake, File Format, Protocol Buffers, Registry, Schema

Creating a Data Lake requires rigor and experience. Here are some good practices around data ingestion both for batch and stream architectures that we recommend and implement with our customers…

David WORMS

Par David WORMS

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Apache Beam: a unified programming model for data processing pipelines

Apache Beam: a unified programming model for data processing pipelines

Catégories : Data Engineering, DataWorks Summit 2018 | Tags : Apex, Beam, Flink, Pipeline, Spark

In this article, we will review the concepts, the history and the future of Apache Beam, that may well become the new standard for data processing pipelines definition. At Dataworks Summit 2018 in…

Gauthier LEONARD

Par Gauthier LEONARD

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What's new in Apache Spark 2.3?

What's new in Apache Spark 2.3?

Catégories : Data Engineering, DataWorks Summit 2018 | Tags : Arrow, PySpark, Tuning, ORC, Spark, Spark MLlib, Data Science, Docker, Kubernetes, pandas, Streaming

Let’s dive into the new features offered by the 2.3 distribution of Apache Spark. This article is a composition of the following talks seen at the DataWorks Summit 2018 and additional research: Apache…

César BEREZOWSKI

Par César BEREZOWSKI

23 mai 2018

Execute Python in an Oozie workflow

Execute Python in an Oozie workflow

Catégories : Data Engineering | Tags : REST, Oozie, Elasticsearch, Python

Oozie workflows allow you to use multiple actions to execute code, however doing so with Python can be a bit tricky, let’s see how to do that. I’ve recently designed a workflow that would interact…

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Oracle DB synchrnozation to Hadoop with CDC

Oracle DB synchrnozation to Hadoop with CDC

Catégories : Data Engineering | Tags : Sqoop, CDC, GoldenGate, Oracle, Hive, Data Warehouse

This note is the result of a discussion about the synchronization of data written in a database to a warehouse stored in Hadoop. Thanks to Claude Daub from GFI who wrote it and who authorizes us to…

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EclairJS - Putting a Spark in Web Apps

EclairJS - Putting a Spark in Web Apps

Catégories : Data Engineering, Front End | Tags : Jupyter, Spark, JavaScript

Presentation by David Fallside from IBM, images extracted from the presentation. Introduction Web Apps development has moved from Java to NodeJS and Javascript. It provides a simple and rich…

David WORMS

Par David WORMS

17 juil. 2016

Splitting HDFS files into multiple hive tables

Splitting HDFS files into multiple hive tables

Catégories : Data Engineering | Tags : Flume, Pig, HDFS, Hive, Oozie, SQL

I am going to show how to split a CSV file stored inside HDFS as multiple Hive tables based on the content of each record. The context is simple. We are using Flume to collect logs from all over our…

David WORMS

Par David WORMS

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Testing the Oracle SQL Connector for Hadoop HDFS

Testing the Oracle SQL Connector for Hadoop HDFS

Catégories : Data Engineering | Tags : Database, File system, Oracle, HDFS, CDH, SQL

Using Oracle SQL Connector for HDFS, you can use Oracle Database to access and analyze data residing in HDFS files or a Hive table. You can also query and join data in HDFS or a Hive table with other…

David WORMS

Par David WORMS

15 juil. 2013

Options to connect and integrate Hadoop with Oracle

Options to connect and integrate Hadoop with Oracle

Catégories : Data Engineering | Tags : Sqoop, Database, Java, Oracle, R, RDBMS, Avro, HDFS, Hive, MapReduce, NoSQL, SQL

I will list the different tools and libraries available to us developers in order to integrate Oracle and Hadoop. The Oracle SQL Connector for HDFS described below is covered in a follow up article…

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Par David WORMS

15 mai 2013

Two Hive UDAF to convert an aggregation to a map

Two Hive UDAF to convert an aggregation to a map

Catégories : Data Engineering | Tags : Java, HBase, Hive, File Format

I am publishing two new Hive UDAF to help with maps in Apache Hive. The source code is available on GitHub in two Java classes: “UDAFToMap” and “UDAFToOrderedMap” or you can download the jar file. The…

David WORMS

Par David WORMS

6 mars 2012

Timeseries storage in Hadoop and Hive

Timeseries storage in Hadoop and Hive

Catégories : Data Engineering | Tags : CRM, timeseries, Tuning, Hadoop, HDFS, Hive, File Format

In the next few weeks, we will be exploring the storage and analytic of a large generated dataset. This dataset is composed of CRM tables associated to one timeserie table of about 7,000 billiard rows…

David WORMS

Par David WORMS

10 janv. 2012

Canada - Maroc - France

Nous sommes une équipe passionnée par l'Open Source, le Big Data et les technologies associées telles que le Cloud, le Data Engineering, la Data Science le DevOps…

Nous fournissons à nos clients un savoir faire reconnu sur la manière d'utiliser les technologies pour convertir leurs cas d'usage en projets exploités en production, sur la façon de réduire les coûts et d'accélérer les livraisons de nouvelles fonctionnalités.

Si vous appréciez la qualité de nos publications, nous vous invitons à nous contacter en vue de coopérer ensemble.

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