Working with a dataset that is too large to fit in memory? Then this post is for you. In this post, we will write memory efficient data pipelines using python generators. We also cover the common generator patterns you will need for your data pipelines.
If you are overwhelmed with re-engineering a legacy data pipeline, then this post is for you. In this post, we go over 6 key principles to help you figure out the most impactful data features for your end user and how to deliver them.
Wondering how to execute a spark job on an AWS EMR cluster, based on a file upload event on S3? Then this post if for you. In this post we go over how to trigger spark jobs on an AWS EMR cluster, using AWS Lambda. The lambda function will execute in response to an S3 upload event. We will go over this event driven pattern with code snippets and set up a fully functioning pipeline.
Unclear data engineering job description ? Wondering what responsibilities falls within a data team ? Then this post is for you. In this post we go over the 6 common responsibilities of a data engineer. The number of these responsibilities that you may end up handling depends on your company and team. Teams in smaller companies generally handle all 6 responsibilities, whereas larger sized companies may have individual(or multiple) teams handling one(or a mix) of these responsibilities.
Setting up an ELT data-ops workflow with multiple environments for developers is often extremely time consuming. What if there was a way to speed up this process, so that you could concentrate on modeling your data and delivering value to your end users? The good news is that there is a way. You can leverage dbt cloud to setup an ELT data-ops workflow in a very short time. In this post, we cover how to setup a data-ops workflow for an ELT system. We will go over how to setup dbt, snowflake, CI and schedule jobs. This data-ops workflow can be easily modified and built upon as your data team's needs evolve.
Spending hundreds of thousands of dollars on vendor BI tools ? Looking for a clean open source alternative ? Then this post is for you. In this post we go over Apache Superset, which is one of the most popular open source visualization tools. We will go over its architecture and build charts and dashboards to visualize data. We will end with a list of pros and cons with using an open source visualization tool like Apache Superset.
Wondering how to store a dimension table's history over time and how to join these historical dimension tables with fact tables for analytical querying ? Then this post is for you. In this post, we will go over a popular dimension modeling technique called SCD2, which preserves historical changes. We will also see how to join a fact table with an SCD2 table to get accurate point in time information.
Whenever updating a few records in an OLTP table we just use the update command. But what if we have to update millions of records in an OLTP table? If you run a large update, your database will lock those records and other transactions may fail. In this post we look at how a large update can cause lock timeout error and how running batches of smaller updates can eliminate this issue.
Using dbt you can test the output of your sql transformations. If you have wondered how to "unit test" your sql transformations in dbt, then this post is for you. In this post, we go over how to write unit tests for your sql transformations with mock inputs/outputs and test them locally. This helps keep the development cycle shorter and enables you to follow a TDD approach for your sql based data pipelines.
Wondering how to backfill an hourly SQL query in Apache Airflow ? Then, this post is for you. In this post we go over how to manipulate the execution_date to run backfills with any time granularity. We use an hourly DAG to explain execution_date and how you can manipulate them using Airflow macros.