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.
Articles associés au Data Engineering
Keycloak deployment in EC2
Catégories : Cloud Computing, Data Engineering, Infrastructure | Tags : Security, EC2, Authentication, AWS, Docker, Keycloak, SSL/TLS, SSO
Why use Keycloak Keycloak is an open-source identity provider (IdP) using single sign-on (SSO). An IdP is a tool to create, maintain, and manage identity information for principals and to provide…
Par Stephan BAUM
14 mars 2023
Big data infrastructure internship
Catégories : Big Data, Data Engineering, DevOps & SRE, Infrastructure | Tags : Infrastructure, Hadoop, Big Data, Cluster, Internship, Kubernetes, TDP
Job description Big Data and distributed computing are at the core of Adaltas. We accompagny our partners in the deployment, maintenance, and optimization of some of the largest clusters in France…
Par Stephan BAUM
2 déc. 2022
Comparison of database architectures: data warehouse, data lake and data lakehouse
Catégories : Big Data, Data Engineering | Tags : Data Governance, Infrastructure, Iceberg, Parquet, Spark, Data Lake, Data 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…
Par Gonzalo ETSE
17 mai 2022
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
Databricks is an optimized data analytics platform based on Apache Spark. Monitoring Databricks plateform is crucial to ensure data quality, job performance, and security issues by limiting access to…
Par Claire PLAYE
10 mai 2022
An overview of Cloudera Data Platform (CDP)
Catégories : Big Data, Cloud Computing, Data Engineering | Tags : SDX, Big Data, Cloud, Cloudera, CDP, CDH, Data Analytics, Data Hub, Data Lake, Data lakehouse, Data Warehouse
Cloudera Data Platform (CDP) is a cloud computing platform for businesses. It provides integrated and multifunctional self-service tools in order to analyze and centralize data. It brings security and…
19 juil. 2021
Self-Paced training from Databricks: a guide to self-enablement on Big Data & AI
Catégories : Data Engineering, Learning | Tags : Cloud, Data Lake, Databricks, Delta Lake, MLflow
Self-paced trainings are proposed by Databricks inside their Academy program. The price is $ 2000 USD for unlimited access to the training courses for a period of 1 year, but also free for customers…
Par Anna KNYAZEVA
26 mai 2021
Find your way into data related Microsoft Azure certifications
Catégories : Cloud Computing, Data Engineering | Tags : Data Governance, Azure, Data Science
Microsoft Azure has certification paths for many technical job roles such as developer, Data Engineer, Data Scientist and solution architect among others. Each of these certifications consists of…
Par Barthelemy NGOM
14 avr. 2021
Apache Liminal: when MLOps meets GitOps
Catégories : Big Data, Containers Orchestration, Data Engineering, Data Science, Tech Radar | Tags : Data Engineering, CI/CD, Data Science, Deep Learning, Deployment, Docker, GitOps, Kubernetes, Machine Learning, MLOps, Open source, Python, TensorFlow
Apache Liminal is an open-source software which proposes a solution to deploy end-to-end Machine Learning pipelines. Indeed it permits to centralize all the steps needed to construct Machine Learning…
Par Aargan COINTEPAS
31 mars 2021
Storage size and generation time in popular file formats
Catégories : Data Engineering, Data Science | Tags : Avro, HDFS, Hive, ORC, Parquet, Big Data, Data Lake, File Format, JavaScript Object Notation (JSON)
Choosing an appropriate file format is essential, whether your data transits on the wire or is stored at rest. Each file format comes with its own advantages and disadvantages. We covered them in a…
Par Barthelemy NGOM
22 mars 2021
TensorFlow Extended (TFX): the components and their functionalities
Catégories : Big Data, Data Engineering, Data Science, Learning | Tags : Beam, Data Engineering, Pipeline, CI/CD, Data Science, Deep Learning, Deployment, Machine Learning, MLOps, Open source, Python, TensorFlow
Putting Machine Learning (ML) and Deep Learning (DL) models in production certainly is a difficult task. It has been recognized as more failure-prone and time consuming than the modeling itself, yet…
5 mars 2021
Connecting to ADLS Gen2 from Hadoop (HDP) and Nifi (HDF)
Catégories : Big Data, Cloud Computing, Data Engineering | Tags : NiFi, Hadoop, HDFS, Authentication, Authorization, Azure, Azure Data Lake Storage (ADLS), OAuth2
As data projects built in the Cloud are becoming more and more frequent, a common use case is to interact with Cloud storage from an existing on premise Big Data platform. Microsoft Azure recently…
Par Gauthier LEONARD
5 nov. 2020
Experiment tracking with MLflow on Databricks Community Edition
Catégories : Data Engineering, Data Science, Learning | Tags : Spark, Databricks, Deep Learning, Delta Lake, Machine Learning, MLflow, Notebook, Python, Scikit-learn
Introduction to Databricks Community Edition and MLflow Every day the number of tools helping Data Scientists to build models faster increases. Consequently, the need to manage the results and the…
10 sept. 2020
Download datasets into HDFS and Hive
Catégories : Big Data, Data Engineering | Tags : Business intelligence, Data Engineering, Data structures, Database, Hadoop, HDFS, Hive, Big Data, Data Analytics, Data Lake, Data lakehouse, Data Warehouse
Introduction Nowadays, the analysis of large amounts of data is becoming more and more possible thanks to Big data technology (Hadoop, Spark,…). This explains the explosion of the data volume and the…
Par Aida NGOM
31 juil. 2020
Comparison of different file formats in Big Data
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
In data processing, there are different types of files formats to store your data sets. Each format has its own pros and cons depending upon the use cases and exists to serve one or several purposes…
Par Aida NGOM
23 juil. 2020
Importing data to Databricks: external tables and Delta Lake
Catégories : Data Engineering, Data Science, Learning | Tags : Parquet, AWS, Amazon S3, Azure Data Lake Storage (ADLS), Databricks, Delta Lake, Python
During a Machine Learning project we need to keep track of the training data we are using. This is important for audit purposes and for assessing the performance of the models, developed at a later…
21 mai 2020
Optimization of Spark applications in Hadoop YARN
Catégories : Data Engineering, Learning | Tags : Tuning, Hadoop, Spark, Python
Apache Spark is an in-memory data processing tool widely used in companies to deal with Big Data issues. Running a Spark application in production requires user-defined resources. This article…
30 mars 2020
MLflow tutorial: an open source Machine Learning (ML) platform
Catégories : Data Engineering, Data Science, Learning | Tags : AWS, Azure, Databricks, Deep Learning, Deployment, Machine Learning, MLflow, MLOps, Python, Scikit-learn
Introduction and principles of MLflow With increasingly cheaper computing power and storage and at the same time increasing data collection in all walks of life, many companies integrated Data Science…
23 mars 2020
Logstash pipelines remote configuration and self-indexing
Catégories : Data Engineering, Infrastructure | Tags : Docker, Elasticsearch, Kibana, Logstash, Log4j
Logstash is a powerful data collection engine that integrates in the Elastic Stack (Elasticsearch - Logstash - Kibana). The goal of this article is to show you how to deploy a fully managed Logstash…
13 déc. 2019
Internship Data Science & Data Engineer - ML in production and streaming data ingestion
Catégories : Data Engineering, Data Science | Tags : Flink, DevOps, Hadoop, HBase, Kafka, Spark, Internship, Kubernetes, Python
Context The exponential evolution of data has turned the industry upside down by redefining data storage, processing and data ingestion pipelines. Mastering these methods considerably facilitates…
Par David WORMS
26 nov. 2019
Insert rows in BigQuery tables with complex columns
Catégories : Cloud Computing, Data Engineering | Tags : GCP, BigQuery, Schema, SQL
Google’s BigQuery is a cloud data warehousing system designed to process enormous volumes of data with several features available. Out of all those features, let’s talk about the support of Struct…
Par César BEREZOWSKI
22 nov. 2019
Machine Learning model deployment
Catégories : Big Data, Data Engineering, Data Science, DevOps & SRE | Tags : DevOps, Operation, AI, Cloud, Machine Learning, MLOps, On-premises, Schema
“Enterprise Machine Learning requires looking at the big picture […] from a data engineering and a data platform perspective,” lectured Justin Norman during the talk on the deployment of Machine…
Par Oskar RYNKIEWICZ
30 sept. 2019
Spark Streaming part 4: clustering with Spark MLlib
Catégories : Data Engineering, Data Science, Learning | Tags : Apache Spark Streaming, Spark, Big Data, Clustering, Machine Learning, Scala, Streaming
Spark MLlib is an Apache’s Spark library offering scalable implementations of various supervised and unsupervised Machine Learning algorithms. Thus, Spark framework can serve as a platform for…
Par Oskar RYNKIEWICZ
27 juin 2019
Spark Streaming part 3: DevOps, tools and tests for Spark applications
Catégories : Big Data, Data Engineering, DevOps & SRE | Tags : Apache Spark Streaming, DevOps, Learning and tutorial, Spark
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…
Par Oskar RYNKIEWICZ
31 mai 2019
Spark Streaming part 2: run Spark Structured Streaming pipelines in Hadoop
Catégories : Data Engineering, Learning | Tags : Apache Spark Streaming, Spark, Python, Streaming
Spark can process streaming data on a multi-node Hadoop cluster relying on HDFS for the storage and YARN for the scheduling of jobs. Thus, Spark Structured Streaming integrates well with Big Data…
Par Oskar RYNKIEWICZ
28 mai 2019
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…
Par Oskar RYNKIEWICZ
18 avr. 2019
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…
Par Oskar RYNKIEWICZ
25 mars 2019
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…
Par César BEREZOWSKI
5 nov. 2018
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…
Par David WORMS
18 juin 2018
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…
Par Gauthier LEONARD
24 mai 2018
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…
Par César BEREZOWSKI
23 mai 2018
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…
Par César BEREZOWSKI
6 mars 2018
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…
Par David WORMS
13 juil. 2017
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…
Par David WORMS
17 juil. 2016
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…
Par David WORMS
15 sept. 2013
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…
Par David WORMS
15 juil. 2013
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…
Par David WORMS
15 mai 2013
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…
Par David WORMS
6 mars 2012
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…
Par David WORMS
10 janv. 2012
