Data Engineering

A series of articles dedicated to Big Data analytics. All codes and exercises of my blog are hosted on GitHub in a dedicated repository :

I’ll start by introducing really general articles :

Understanding Computer Components : In order to fully understand the cloud concepts, I wrote a quick article on the main components of computers.

Useful Bash commands : A quick recap of useful Bash commands to interact with your Terminal.

Making your code production-ready : A quick overview of nice tools to improve the quality of your code before production !


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Introduction to Hadoop : In this article, we’ll cover the basics of Hadoop, a quick history, and an overview of the main features of Hadoop.

MapReduce : MapReduce is a programming paradigm that allows to process a large amount of data by initially splitting the data into blocks, sending the blocks to different clusters to perform operations, and aggregating the results.

HDFS : HDFS stands for Hadoop Distributed File System. It is a sub-project of Hadoop. HDFS lets you connect nodes contained within clusters over which data files are distributed, overall being fault-tolerant.

VM’s in Virtual Box : Hadoop runs only on GNU/Linux platforms. Therefore, if you have another OS, you need to install Virtual Box. Virtual Box is a software that lets you create and run Virtual Machines.

Hadoop with the HortonWorks Sandbox : The Sandbox by Hortonworks is a straightforward, pre-configured, learning environment that contains the latest developments from Apache Hadoop, specifically the Hortonworks Data Platform (HDP).

Load and move files to HDFS : In this article, we’ll see how to download the input text file for our WordCount job, and put the file into HDFS.

Launch a Map Reduce Job : In this article, we’ll get to the core of the exercise : launch a MapReduce WordCount job.

MapReduce Jobs in Python : Using Hadoop Streaming, we can submit MapReduce Jobs in Python.

Execute MapReduce Job in Python locally : In this short article, we’ll see how to execute MapReduce Jobs using Hadoop streaming locally.


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Introduction to Spark : Apache Spark is a unified analytics engine for large-scale data processing. It is overall much faster than Hadoop MapReduce, and widely used in the industry.

Install Spark-Scala and PySpark : In this quick tutorial, we’ll cover installation techniques for the 2 most popular APIs of Spark.

Using Spark-Scala for Machine Learning : A dive into Spark-Scala for Machine Learning through examples.


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Cloud Concepts : What is Cloud Computing ? What are the main concepts behind it ? This article is part of the AWS Cloud Practioner certification.

Core Services : What are the core services of AWS and other Cloud providers ?


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TPU survival guide on Google Colaboratory : TPUs are changing the way we are training Neural Networks. Developped by Google, TPUs have a different way to perform matrix operations and are optimized for certain tasks. We’ll cover the basics of TPU computation, and how to use them in Colab.

Using Google Drive to save and load files in Colab : Google Colab offers CPUs, GPUs and TPUs for free. It’s a great tool for any project, but you need to have your files uploaded on Google Drive. We’ll see how to interact with Google Drive from Colab.

Deploy a container on GCP : A simple step by step tutorial on how to deploy a container on Google Cloud Platform.

The articles below are part of the Google Cloud Platform Data Engineering Specialization on Coursera :

Course 1: Google Cloud Platform Big Data and Machine Learning Fundamentals

Introduction to GCP (Week 1 Module 1) : Introduction to Google Cloud Platform and its services.

Lab on Compute Engine (Week 1 Module 1) : Create a small webapp to display earthquake data publicly using Compute Engine and Storage Buckets.

Lab on BigQuery (Week 1 Module 1) : Explore a Public Dataset with BigQuery, and import our own data.

Introduction to Recommendation Systems (Week 1 Module 2) : E-commerce is probably the most common recommendation systems that we encounter. Models learn what we may like based on our preferences.

Run Spark jobs on Cloud DataProc (Week 1 Module 2) : Running a Pi-estimate Spark job using Cloud DataProc.

Lab - Recommend products using Cloud SQL and SparkML : Execute SparkML jobs and recommend products using CloudSQL.

Run ML models in SQL with BigQuery ML (Week 1 Module 3) : BigQuery ML is a cool feature to run linear or logistic regressions in SQL.

Create Streaming Data Pipelines (Week 2 Module 1) : Discover Cloud DataFlow, Apache Beam, Cloud Pub/Sub…

Lab - Create a streaming data pipeline with Cloud DataFlow : Ingest real-time data with Cloud Dataflow, analyze it in BigQuery, explore it in DataStudio.

Classify Images using Vision API and Cloud AutoML (Week 2 Module 2) : An introduction to ML solutions for unstructured data in GCP.

Course 2: Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform

Introduction to Cloud Dataproc (Week 1 Module 1) : Running Hadoop Clusters and submitting jobs has never been easier.

Lab - Create a Cloud DataProc Cluster : A full guide to creating a cluster on Cloud Dataproc.

Run jobs on Dataproc (Week 1 Module 2) : How to run jobs on Dataproc using Pig, Hive or Spark?

Course 3: Serverless Data Analysis with Google BigQuery and Cloud Dataflow

Course 4: Serverless Machine Learning with Tensorflow on Google Cloud Platform

Course 5: Building Resilient Streaming Systems on Google Cloud Platform


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Deploy a Streamlit WebApp with Docker : Build and run a container from a simple Named Entity Recognition web application.

Deploy a container on GCP : Deploy the container to DockerHub and run it on GCP !

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Introduction to the Elastic Stack : What is the Elastic Stack ? What are the main products and features of ELK ?

Getting started with Elastic Cloud and Kibana : Elastic Cloud allows on demand cluster deployment. It is an easy way to deploy your platform within seconds, and start visualization with Kibana.

Install and run Elasticsearch + Kibana locally : How can you install Elasticsearch and Kibana locally ? We’ll also cover a Heartbeat metric for your computer.

Working with Dev Tools in Elasticsearch : DevTools is the default interpreter to interact with your data in Elasticsearch. We’ll cover the basic queries and data manipulations.


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Introduction to Graphs : What is a graph ? Where are graphs being used ? What are the components of a graph ?

Graph Analysis, Erdos-Rényi, Barabasi-Albert : In this article, we cover the two main types of graphs, and describe a first approach to graph analysis.

Graph Algorithms : We’ll now explore the main graph algorithms and several use cases in a visual way with direct examples in Python.

Graph Learning : How can we handle missing links or missing nodes in graphs ?

Introduction to Neo4J and Graph Databases : Neo4J is a graph oriented database that is widely used for anomaly detection or real time recommendation. We’ll cover the basics of Neo4J here.

A day at the Neo4J Graph Tour : I had the opportunity to attend the Neo4J Graph Tour 2019. Here’s what I learned.


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Install Zeppelin Locally : How to easily install Zeppelin on your local machine ?

Run a Zeppelin Notebook in AWS EMR : Running Zeppelin locally is good. But at the time of Big Data analytics, how can you launch Zeppelin on AWS EMR ?

Working with Amazon S3 Buckets : S3 buckets offer great storage solutions for your Big Data projects. We’ll see how to get a simple example to work.

Launch and access an AWS EC2 Cluster : A quick overview of how to work with AWS EC2 and establish SSH connection with it.

Install Apache Cassandra on an AWS EC2 Cluster : Cassandra is a No-SQL approach store large amount of data. In this article, we’ll see how to install it on your EC2 cluster.

Install Zookeeper on EC2 instances : How can you install Zookeeper on EC2 instances for a resilient architecture ?

Install Apache Spark on EC2 instances : An article on how to install Spark on EC2 instances and get your Big Data platform ready !

Big (Open) Data , the GDELT Project : We’ll introduce GDELT, an open source database with several Tb of data available.

Build an ETL in Scala for GDELT Data : How can you process such a large amount of data using Scala and S3 ?

Move Scala Dataframes to Cassandra : Once the data has been processed, how can you move it to Cassandra ?



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