Have you missed any cloud data engineering-related news in the last 3 months? No worries, I got you covered with the new part of the "What's new on the cloud for data engineers..." series.
There is always a gap between a disruption in the data engineering industry and its integration on the cloud. It was not different for table file formats which have started gaining interest on AWS, Azure, GCP recently.
It's the last update on the data engineering news on the cloud this year. There are a lot of things coming out. Especially for the streaming processing!
Setting a data processing layer up has several phases. You need to write the job, define the infrastructure, CI/CD pipeline, integrate with the data orchestration layer, ... and finally, ensure the job can access the relevant datasets. The most basic authentication mechanism uses login/password pair but can we do better on the cloud? Let's see!
Four months in cloud history is a huge period of time. Even when 2 of the 4 months are the usual "holiday" months. As you can guess from the title, it's time to see what changed recently on the cloud from a data engineering perspective!
It's time for the first cloud news blog post this year. The update summary lists all changes of data or data-related services between January 1 and April 25.
Data ingestion is the starting point for all data systems. It can work in batch or streaming mode. I've recently covered the batch ingestion pretty much already with previous blog posts but I haven't done anything for the streaming, yet. Until today when you can read a few words about HTTP-based data ingestion to cloud streaming brokers.
Data is a live being. It's getting queried, written, overwritten, backfilled and ... migrated. Since the last point is the least obvious from the list, I've recently spent some time trying to understand it better in the context of the cloud.
Data migration is one of the scenarios you can face as a data engineer. It's not always an easy task but managed cloud services can help you to put in place the pipeline and solve many common problems.
The volume of the data to migrate from an on-premise to a cloud environment will probably be less significant than previous years since a lot of organizations are already on the cloud. However, it's interesting to see different methods to bring the data there and that's something I'll show you in this blog post.
Writing data processing jobs is a fascinating task. But it can't be worthless if the users can't find and use the generated data. Fortunately, we can count on data catalogs and leverage the power of metadata to overcome this discoverability issue.
When I've first met the Complex Event Processing (CEP) term, I was scared. Event streaming processing itself was complex enough, so why this extra complex-specific stuff? It happens that the complexity is real but in this post I will rather focus on a different aspect. What are the services supporting the CEP on the cloud?
AWS was the first cloud provider I've been working on. That's why when I did my first Azure and GCP project, I was always asking myself, "Hey, how would you implement that on AWS?". Answering that question was easy most of the time, but sometimes I got stuck. One of my most significant issues was the identity and permissions management component. I will try to share some related answers in this blog post.
Data is not perfect, and in each project, you'll probably need to do some cleaning to prepare it for business use cases. To make this task easier, cloud providers have dedicated data wrangling services, and they'll be the topic of this blog post.
It's time for the 5th part of the "What's new on the cloud for data engineers" series. This time I will cover the changes between September and December.
When I tell you "schema management" and "streaming", you'll certainly think about the schema registry of Apache Kafka. That's true but also streaming cloud services do manage the schemas and in this blog post we'll see how.
That's one of the biggest problems I've faced in my whole career. The development environment! I'm not talking here about creating cloud resources in different subscription but about the environment sharing similar characteristics to the production. In the blog post I'll share with you different strategies to put in place in the context of the cloud and streaming applications.
Processing static datasets is easier than dynamic ones that may change in time. Hopefully, cloud services offer various more and less manual features to scale the data processing logic. We'll see some of them in this blog post.
How to manage secrets is probably one of the first problems you may encounter while deploying some resources from a CI/CD pipeline. The simple answer is: not manage them at all! Let the cloud services do this.
One of the big announcements of the previous Data+AI Summit was Delta Sharing, a protocol to exchange the life data with internal and external users. The question I asked myself at that moment was "Does it exist on the cloud?". Let's see.