Python
Python is a dynamic, interpretive and scripted programming language. It was developed at the beginning of the 1990s by Guido van Rossum. Today, this language is being developed as an open-source project by many developers worldwide, led by Guido through the Python Software Foundation (PSF). The goal of the language is to develop an easy-to-learn programming language producing an intuitive and readable code while remaining as powerful as other established programming languages.
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Categories: Big Data, Infrastructure | Tags: Programming, Ansible, Hadoop, Python, TDP
All the deployments are automated and Ansible plays a central role. With the growing complexity of the code base, a new system was needed to overcome the Ansible limitations which will enable us to…
Jan 24, 2023
Spark on Hadoop integration with Jupyter
Categories: Adaltas Summit 2021, Infrastructure, Tech Radar | Tags: YARN, HDP, Infrastructure, Jupyter, Spark, CDP, Notebook, TDP
For several years, Jupyter notebook has established itself as the notebook solution in the Python universe. Historically, Jupyter is the tool of choice for data scientists who mainly develop in Python…
Sep 1, 2022
Ansible variables: choosing the right location
Categories: DevOps & SRE | Tags: Infrastructure, Ansible, IaC, YAML
Defining variables for your Ansible playbooks and roles can become challenging as your project grows. Browsing the Ansible documentation, the diversity of Ansible variables location is confusing, to…
Mar 15, 2022
H2O in practice: a protocol combining AutoML with traditional modeling approaches
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python, XGBoost
H20 comes with a lot of functionalities. The second part of the series H2O in practice proposes a protocol to combine AutoML modeling with traditional modeling and optimization approach. The objective…
Nov 12, 2021
H2O in practice: a Data Scientist feedback
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python
Automated machine learning (AutoML) platforms are gaining popularity and becoming a new important tool in the data scientists’ toolbox. A few months ago, I introduced H2O, an open-source platform for…
Sep 29, 2021
Modern Python part 3: run a CI pipeline & publish your package to PiPy
Categories: DevOps & SRE | Tags: GitHub, CI/CD, Git, Python, Release and features, Unit tests
To propose a well-maintained and usable Python package to the open-source community or even inside your company, you are expected to accomplish a set of critical steps. First ensure that your code is…
By Faouzi BRAZA
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Modern Python part 2: write unit tests & enforce Git commit conventions
Categories: DevOps & SRE | Tags: Git, pandas, Python, Unit tests
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By Faouzi BRAZA
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Modern Python part 1: start a project with pyenv & poetry
Categories: DevOps & SRE | Tags: Git, Python, Release and features, Unit tests
When learning a programming language, the focus is essentially on understanding the syntax, the code style, and the underlying concepts. With time, you become sufficiently comfortable with the…
By Faouzi BRAZA
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Apache Liminal: when MLOps meets GitOps
Categories: 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…
Mar 31, 2021
TensorFlow Extended (TFX): the components and their functionalities
Categories: 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…
Mar 5, 2021
Faster model development with H2O AutoML and Flow
Categories: Data Science, Learning | Tags: Automation, Cloud, H2O, Machine Learning, MLOps, On-premises, Open source, Python
Building Machine Learning (ML) models is a time-consuming process. It requires expertise in statistics, ML algorithms, and programming. On top of that, it also requires the ability to translate a…
Dec 10, 2020
Experiment tracking with MLflow on Databricks Community Edition
Categories: 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…
Sep 10, 2020
Importing data to Databricks: external tables and Delta Lake
Categories: 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…
May 21, 2020
Introducing Apache Airflow on AWS
Categories: Big Data, Cloud Computing, Containers Orchestration | Tags: PySpark, Learning and tutorial, Airflow, Oozie, Spark, AWS, Docker, Python
Apache Airflow offers a potential solution to the growing challenge of managing an increasingly complex landscape of data management tools, scripts and analytics processes. It is an open-source…
May 5, 2020
Optimization of Spark applications in Hadoop YARN
Categories: 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…
Mar 30, 2020
MLflow tutorial: an open source Machine Learning (ML) platform
Categories: 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…
Mar 23, 2020
Introduction to Ludwig and how to deploy a Deep Learning model via Flask
Categories: Data Science, Tech Radar | Tags: Learning and tutorial, Deep Learning, Ludwig Deep Learning Toolbox, Machine Learning, Python
Over the past decade, Machine Learning and deep learning models have proven to be very effective in performing a wide variety of tasks such as fraud detection, product recommendation, autonomous…
Mar 2, 2020
Spark Streaming part 3: DevOps, tools and tests for Spark applications
Categories: 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…
May 31, 2019
Spark Streaming part 2: run Spark Structured Streaming pipelines in Hadoop
Categories: 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…
May 28, 2019
Internship Data Science & Data Engineer - ML in production and streaming data ingestion
Categories: 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…
By David WORMS
Nov 26, 2019
Machine Learning model deployment
Categories: 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…
Sep 30, 2019
TensorFlow installation on Docker
Categories: Containers Orchestration, Data Science, Learning | Tags: CPU, Jupyter, Linux, AI, Deep Learning, Docker, TensorFlow
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Aug 5, 2019
Running Apache Hive 3, new features and tips and tricks
Categories: Big Data, Business Intelligence, DataWorks Summit 2019 | Tags: Druid, JDBC, LLAP, Hadoop, Hive, Kafka, Release and features
Apache Hive 3 brings a bunch of new and nice features to the data warehouse. Unfortunately, like many major FOSS releases, it comes with a few bugs and not much documentation. It is available since…
Jul 25, 2019
Auto-scaling Druid with Kubernetes
Categories: Big Data, Business Intelligence, Containers Orchestration | Tags: Druid, CNCF, Helm, Metrics, OLAP, Operation, Container Orchestration, EC2, Cloud, Data Analytics, Kubernetes, Prometheus, Python
Apache Druid is an open-source analytics data store which could leverage the auto-scaling abilities of Kubernetes due to its distributed nature and its reliance on memory. I was inspired by the talk…
Jul 16, 2019
First Class Functions in Python
Categories: Hack, Learning | Tags: Programming, Python
I recently watched a talk by Dave Cheney about first class functions in Go. Python supports first class functions too, so can we use them in the same ways? Absolutely. I have been using Python for a…
Apr 15, 2019
Publish Spark SQL DataFrame and RDD with Spark Thrift Server
Categories: 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…
Mar 25, 2019
Applying Deep Reinforcement Learning to Poker
Categories: Data Science | Tags: Algorithm, Gaming, Q-learning, Deep Learning, Machine Learning, Neural Network, Python
We will cover the subject of Deep Reinforcement Learning, more specifically the Deep Q Learning algorithm introduced by DeepMind, and then we’ll apply a version of this algorithm to the game of Poker…
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Monitoring a production Hadoop cluster with Kubernetes
Categories: DevOps & SRE | Tags: Thrift, Grafana, Shinken, Hadoop, Knox, Cluster, Docker, Elasticsearch, Kubernetes, Node, Node.js, Prometheus, Python
Monitoring a production grade Hadoop cluster is a real challenge and needs to be constantly evolving. The software we use today is based on Nagios. Very efficient when it comes to the simplest…
Dec 21, 2018
CodaLab – Data Science competitions
Categories: Data Science, Adaltas Summit 2018, Learning | Tags: Database, Infrastructure, Machine Learning, MySQL, Node.js, Python
CodaLab Competition is a platform for code execution in the field of Data Science. It is a web interface on which a user can submit code or results and compare themselves to others. Let’s see how it…
Dec 17, 2018
Jumbo, the Hadoop cluster bootstrapper
Categories: Infrastructure | Tags: Ambari, Automation, HDP, REST, Ansible, Cluster, Vagrant
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Nov 29, 2018
Lando: Deep Learning used to summarize conversations
Categories: Data Science, Learning | Tags: Micro Services, Open API, Deep Learning, Internship, Kubernetes, Neural Network, Node.js
Lando is an application to summarize conversations using Speech To Text to translate the written record of a meeting into text and Deep Learning technics to summarize contents. It allows users to…
By Yliess HATI
Sep 18, 2018
TensorFlow on Spark 2.3: The Best of Both Worlds
Categories: Data Science, DataWorks Summit 2018 | Tags: Mesos, YARN, C++, CPU, GPU, Tuning, Spark, JavaScript, Keras, Kubernetes, Machine Learning, Python, TensorFlow
The integration of TensorFlow With Spark has a lot of potential and creates new opportunities. This article is based on a conference seen at the DataWorks Summit 2018 in Berlin. It was about the new…
By Yliess HATI
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Apache Beam: a unified programming model for data processing pipelines
Categories: 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…
May 24, 2018
What's new in Apache Spark 2.3?
Categories: Data Engineering, DataWorks Summit 2018 | Tags: Arrow, PySpark, Tuning, ORC, Spark, Spark MLlib, Data Science, Docker, Kubernetes, pandas, Streaming
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May 23, 2018
Present and future of Hadoop workflow scheduling: Oozie 5.x
Categories: Big Data, DataWorks Summit 2018 | Tags: Sqoop, HDP, REST, Hadoop, Hive, Oozie, CDH
During the DataWorks Summit Europe 2018 in Berlin, I had the opportunity to attend a breakout session on Apache Oozie. It covers the new features released in Oozie 5.0, including future features of…
May 23, 2018
Execute Python in an Oozie workflow
Categories: 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…
Mar 6, 2018
From Dockerfile to Ansible Containers
Categories: Containers Orchestration, DevOps & SRE, Open Source Summit Europe 2017 | Tags: pip, Shell, Ansible, Docker, Docker Compose, YAML
This talk was an introduction to the Dockerfile format and to Ansible container’s tool and then a comparison of both. It was hold by Tomas Tomecek from Red Hat’s containerization team. The Dockerfile…
Oct 25, 2017
Cloudera Sessions Paris 2017
Categories: Big Data, Events | Tags: Altus, CDSW, SDX, PaaS, EC2, Azure, Cloudera, CDH, Data Science
Adaltas was at the Cloudera Sessions on October 5, where Cloudera showcased their new products and offerings. Below you’ll find a summary of what we witnessed. Note: the information were aggregated in…
Oct 16, 2017
HDP cluster monitoring
Categories: Big Data, DevOps & SRE, Infrastructure | Tags: Alert, Ambari, HDP, Metrics, Monitoring, REST
With the current growth of BigData technologies, more and more companies are building their own clusters in hope to get some value of their data. One main concern while building these infrastructures…
Jul 5, 2017
Get in control of your workflows with Apache Airflow
Categories: Big Data, Tech Radar | Tags: DevOps, Airflow, Cloud, Python
Below is a compilation of my notes taken during the presentation of Apache Airflow by Christian Trebing from BlueYonder. Introduction Use case: how to handle data coming in regularly from customers…
Jul 17, 2016
Splitting HDFS files into multiple hive tables
Categories: 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…
By David WORMS
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E-commerce electronic cigarettes: first impressions with Prestashop
Categories: Tech Radar | Tags: HTML, Java, Node.js
Last year, I had to select and integrate an e-commerce software for the website CigarHit selling electronic cigarettes. Considering that the last e-commerce integration I made dated from 2005, I took…
By David WORMS
Jul 25, 2012
Node.js is now integrated to the Microsoft Azure platform
Categories: Cloud Computing, Tech Radar | Tags: Linux, Azure, Cloud, Node.js
Node is now a first class citizen in the Microsoft Azure cloud environment alongside .Net, Java and PHP. This integration is the logical consequence of Microsoft’s involvement in the development of…
By David WORMS
Dec 11, 2011
Node.js, JavaScript on the server side
Categories: Front End, Node.js | Tags: HTTP, Server, JavaScript, Node.js
Waiting for the Next Big Language (NBL for Next Big Language), this is now 3 years or more since I predict to my customers a bright future for JavaScript as a programming language for server…
By David WORMS
Jun 12, 2010
