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Data engineering on AWS Data engineering on Azure Data engineering on GCP Data engineering on the cloud
If not, below you can find all articles belonging to Cloud.
During my study of BigQuery I found an ETL pattern called feedback loop. Since I never heard about it before, I decided to spend some time and search for other ETL patterns and the cloud features we could use in them.
Cloud computing is present in my life for 4 years and I never found a good system to keep myself up to date. It's even more critical at this moment, when I'm trying to follow what happens on the 3 major providers (AWS, Azure, GCP). Since blogging helped me to achieve that for Apache Spark, and by the way learn from you, I'm gonna try the same solution for the cloud.
I had a chance to use, for a longer or shorter period of time, 3 different cloud providers. In this post I would like to share with you, how my perfect cloud provider could look like.
When I came to the data world, I had no idea what the data governance was. One of the tools which helped me to understand that was AWS Glue. I had a chance to work on it again during my AWS Big Data specialty exam preparation and that's at this moment I asked myself - "DynamicFrames?! What's the difference with DataFrames?" In this post I'll try to shed light on it.
When I was preparing my AWS Big Data specialty certification, I was not comfortable with 2 categories, the visualization and security. Because of that I decided to work on them, starting with the latter one which can have a more direct impact. The work that I initiate with this post about data security practices on AWS data services.
January 10, 2020 I successfully passed my AWS Certified Big Data specialty with the overall score of 82%. Despite the fact that it will be replaced soon (April 2020) by AWS Certified Data Analytics - Specialty, I'd like to share with you my learning process and interesting resources.
One of goals in my 3-Levels List was to get 3 certificates: AWS Cloud Practitioner, AWS Big Data and GCP Data Engineer. I've already passed the first one and that's the reason I'm writing this blog post.
One of approaches to load big volumes of data efficiently is to use bulk operations. The idea is to take all the records and put them into data store at once. For this purpose, AWS Redshift exposes an operation called COPY.
Last time I wrote about sending Apache Kafka data to batch layer. This time I would like to do the same but with AWS technologies, namely Kinesis, Firehose and S3.
I really appreciate AWS services and one of the main reasons for that is the facility to implement event-driven systems. One of the interesting use cases of these events is related to the EMR service, responsible for running Apache Spark pipelines. In this post I will try to associate an action invoked every time an EMR step completes successfully.
Despite the recent critics (cf. "Serverless Computing: One Step Forward, Two Steps Back" link in the Read also section), serverless movement gains the popularity. Databricks proposes a serverless platform for running Apache Spark workflows, Google Cloud Platform comes with a similar service reserved to Dataflow pipelines and Amazon Web Services, ... In this post, I will summarize the good and bad sides of my recent experiences with AWS Lambda applied to the data processing.
Open Source provides a lot of interesting tools to deal with Big Data: Apache Spark, Apache Kafka, Parquet - to quote only a few of them. However nowadays data platforms without cloud support are more and rarer. It's why this topic merits its own category and posts on this blog. To not go too quickly, the first article speaks about services you can use to work with the data on AWS.
