Notes about Cutting-Edge Technologies and Everything

Open-source data integration started 16 years ago with Talend. Since then, the whole industry has changed. Let's compare the different actors. Open-source data integration is not new. It started 16 years ago with Talend. But since then, the whole industry has changed. The likes of Snowflake, Bigquery, Redshift have changed how data is being hosted, managed, and accessed while making it easier and a lot cheaper. But the data integration industry has evolved as well. On one hand, new open-source projects emerged, such as Singer.io in 2017. This enabled more data integration connectors to become accessible to more teams, even though it still required a significant amount of manual work.  On the other hand, data integration was made accessible to more teams (analysts, scientists, business intelligence teams). Indeed, companies like Fivetran benefited from Snowflake’s rise,  empowering non-engineering teams to set up and manage their data integration connectors by themselves, so th...

Airflow is a historically important tool in the data engineering ecosystem. It introduced the ability to combine a strict Directed Acyclic Graph (DAG) model with Pythonic flexibility in a way that made it appropriate for a wide variety of use cases. However, Airflow’s applicability is limited by its legacy as a monolithic batch scheduler aimed at data engineers principally concerned with orchestrating third-party systems employed by others in their organizations. Today, many data engineers are working more directly with their analytical counterparts. Compute and storage are cheap, so friction is low and experimentation prevails. Processes are fast, dynamic, and unpredictable. Airflow got many things right, but its core assumptions never anticipated the rich variety of data applications that has emerged. It simply does not have the requisite vocabulary to describe many of those activities. The seed that would grow into Prefect was first planted all the way back in 2016, in a seri...

Apache Spark is a foundational piece of Uber’s Big Data infrastructure that powers many critical aspects of our business. We currently run more than one hundred thousand Spark applications per day, across multiple different compute environments. Spark’s versatility, which allows us to build applications and run them everywhere that we need, makes this scale possible. However, our ever-growing infrastructure means that these environments are constantly changing, making it increasingly difficult for both new and existing users to give their applications reliable access to data sources, compute resources, and supporting tools. Also, as the number of users grow, it becomes more challenging for the data team to communicate these environmental changes to users, and for us to understand exactly how Spark is being used. We built the Uber Spark Compute Service (uSCS) to help manage the complexities of running Spark at this scale. This Spark-as-a-service solution leverages Apache Livy, cu...

Here’s a curated list of resources for data engineers, with sections for algorithms and data structures, SQL, databases, programming, tools, distributed systems, and more. Useful articles The AI Hierarchy of Needs The Rise of Data Engineer The Downfall of the Data Engineer A Beginner’s Guide to Data Engineering Part I Part II Part III Functional Data Engineering — a modern paradigm for batch data processing How to become a Data Engineer (in Russian) Talks Data Engineering Principles - Build frameworks not pipelines by Gatis Seja Functional Data Engineering - A Set of Best Practices by Maxime Beauchemin Advanced Data Engineering Patterns with Apache Airflow by Maxime Beauchemin Creating a Data Engineering Culture by Jesse Anderson Algorithms & Data Structures Algorithmic Toolbox in Russian Data Structures in Russian Data Structures & Algorithms Specialization on Coursera Algorithms Specialization from Stanford on Coursera SQL Com...

In this post, I am going to discuss Apache Airflow, a workflow management system developed by Airbnb. Earlier I had discussed writing basic ETL pipelines in Bonobo. Bonobo is cool for write ETL pipelines but the world is not all about writing ETL pipelines to automate things. There are other use cases in which you have to perform tasks in a certain order once or periodically. For instance: Monitoring Cron jobs transferring data from one place to other. Automating your DevOps operations. Periodically fetching data from websites and update the database for your awesome price comparison system. Data processing for recommendation based systems. Machine Learning Pipelines. Possibilities are endless. Before we move on further to implement Airflow in our systems, let’s discuss what actually is Airflow and it’s terminologies. What is Airflow? From the Website: Airflow is a platform to programmatically author, schedule and monitor workflows....

Real data behaves in many unexpected ways that can break even the most well-engineered data pipelines. To catch as much of this weird behaviour as possible before users are affected, the ING Wholesale Banking Advanced Analytics team has created 7 layers of data testing that they use in their CI setup and Apache Airflow pipelines to stay in control of their data. The 7 layers are: Original image courtesy of Columbia Spectator ( http://spc.columbiaspectator.com/spectrum/2016/03/31/nine-circles-columbia-hell ) DAG Integrity Tests ; have your CI (Continuous Integration) check if you DAG is an actual DAG Split your ingestion from your deployment ; keep the logic you use to ingest data separate from the logic that deploys your application Data Tests ; check if your logic is outputting what you’d expect Alerting ; get slack alerts from your data pipelines when they blow up Git Enforcing ; always make sure you’re running your latest verified code Mock Pipeline Tests ; create ...