In this post, we go over 6 key concepts to help you master window functions. Window functions are one the most powerful features of SQL, they are very useful in analytics and performing operations that cannot be done easily with the standard group by, subquery and filters. Despite this, window functions are not used frequently. If you have ever thought 'window functions are confusing', then this post is for you.
If you are looking for an easy to setup and simple way to automate, schedule and monitor a 'small' API data pull on the cloud, serverless functions are a good option. In this post we cover what a serverless function can and cannot do, what its pros and cons are and walk through a simple API data pull project. We will be using AWS Lambda and AWS S3 for this project.
There are many ways to submit an Apache Spark job to an AWS EMR cluster using Apache Airflow. In this post we go over the steps on how to create a temporary EMR cluster, submit jobs to it, wait for the jobs to complete and terminate the cluster, the Airflow-way.
Data engineering project for beginners, stream edition. In this post we design and build a simple data streaming pipeline using Apache Kafka, Apache Flink and PostgreSQL DB. We will also review the design and understand some common issues to avoid while building distributed stream processing systems.
This post goes over what the ETL and ELT data pipeline paradigms are. It tries to address the inconsistency in naming conventions and how to understand what they really mean. Finally ends with a comparison of the 2 paradigms and how to use these concepts to build efficient and scalable data pipelines.
This post goes over what exactly a staging area is in a data pipeline. It covers the reasons for having a staging area and goes over some common use cases where having a staging area can save on engineering effort and time.
This post goes over what the term data warehousing means. This post provides a simple e-commerce relational data model and how it has to be changed to fit analytical queries. It also covers the reasoning behind wanting to use a data warehouse and how to choose an appropriate database for your project.
Ensure your data meets basic and business specific data quality constraints. In this post we go over a data quality testing framework called great expectations, which provides powerful functionality to cover the most common test cases and the ability to group them together and run them.
With the advent of powerful data warehouses like snowflake, bigquery, redshift spectrum, etc that allow separation of storage and execution, it has become very economical to store data in the data warehouse and then transform them as required. This post goes over how to design such a ELT system using stitch and DBT. The main objective is to keep the code complexity and server management low, while automating as much as possible
This post covers key techniques to optimize your Apache Spark code. You will know exactly what distributed data storage and distributed data processing systems are, how they operate and how to use them efficiently. Go beyond the basic syntax and learn 3 powerful strategies to drastically improve the performance of your Apache Spark project.