When I ran the Lambda from my previous post against Chariot’s CloudTrail repository, it took almost four minutes to process a single day’s worth of data. That seems like a long time, and as a developer I want to optimize everything I write. In this post I look into analyzing the current runtime, and options for improving it.
As I’ve written in the past, large numbers of small files make for an inefficient data lake. But sometimes, you can’t avoid small files. Our CloudTrail repository, for example, has 4,601,675 files as-of this morning, 44% of which are under 1,000 bytes long. In this post, I develop a Lambda-based data pipeline to aggregate these files, storing them in a new S3 location partitioned by date. Along the way I call out some of the challenges that face such a pipeline.
We asked our team at Chariot about common omnichannel pitfalls, and how companies of any size can overcome or avoid them.
In this week’s TechChat, we welcome Keith Gregory, our Cloud & Data Engineering Practice Lead here at Chariot. Keith is a prolific writer both on the Chariot blog as well as on his own, and is a wealth of knowledge on all things AWS. We touch on Redshift execution plans, how to appropriately size Redshift … Read More
In my “Friends Don’t Let Friends Use JSON” post, I noted that I preferred the Avro file format to Parquet, because it was easier to write code to use it. I expected some pushback, and got it: Parquet is “much” more performant. So I decided to do some benchmarking.
Decision support databases have a number of quirks that are not obvious to the casual user, particularly someone coming from an OLTP background. In this post I look at how unbalanced distributions can impact your query performance, how you can identify imbalances, and what you can do to fix them.
In my last post I recommended using Avro for file storage in a data lake. It has the benefits of compact storage and a schema in every file that tells you what data it holds. In this post I show three ways to generate Avro files: one in Java, and two in Python.
I’ve never been a JSON hater, but I’ve recently run into enough pain with JSON as a data serialization format that my feelings are edging toward dislike. However, JSON is a fact of life in most data pipelines, especially those that receive event-stream data from a third-party supplier. This post reflects on some of the problems that I’ve seen, and solutions that I’ve used
More, cheaper, faster: our own Keith Gregory recounts the changes in big data, data storage, and data engineering over the last two decades.