From Monolithic Architecture to Microservices and Event-Driven Systems

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I’m a massive fan of streaming and real time data processing and solutions. I strongly believe a lot of use cases are going to be defined and implemented around fast and streaming data in near future, especially in IoT and streaming analytics. With 5G rolling out soon and its superfast bandwidth and wide geographical coverage, … Continue reading From Monolithic Architecture to Microservices and Event-Driven Systems

AWS Glue Part 3: Automate Data Onboarding for Your AWS Data Lake

Choosing the right approach to populate a data lake is usually one of the first decisions made by architecture teams after deciding the technology to build their data lake with. A recent trend seems to be taking over is using Spark, since it’s fast and powerful and comes with a lot of flexibilities when used … Continue reading AWS Glue Part 3: Automate Data Onboarding for Your AWS Data Lake

AWS Glue Part 2: ETL your data and query the result in Athena

In part one of my posts on AWS Glue, we saw how Crawlers could be used to traverse data in s3 and catalogue them in AWS Athena. Glue is a serverless service that could be used to create ETL jobs, schedule and run them. In this post we'll create an ETL job using Glue, execute … Continue reading AWS Glue Part 2: ETL your data and query the result in Athena

Airflow & Celery on Redis: when Airflow picks up old task instances

This is going to be a quick post on Airflow. We realized that in one of our environments, Airflow scheduler picks up old task instances that were already a success (whether marked as success or completed successfully). You can verify this is actually your issue by ssh into your Airflow workers, and run: ps -ef … Continue reading Airflow & Celery on Redis: when Airflow picks up old task instances

How to import spark.implicits._ in Spark 2.2: error “value toDS is not a member of org.apache.spark.rdd.RDD”

I wrote about how to import implicits in spark 1.6 more than 2 years ago. But things have changed in Spark 2.2: the first thing you need to do when coding in Spark 2.2 is to set up an SparkSession object. SparkSession is the entry point to programming Spark with DataSet and DataFrame. Like Spark … Continue reading How to import spark.implicits._ in Spark 2.2: error “value toDS is not a member of org.apache.spark.rdd.RDD”

Spark Error “java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE” in Spark 1.6

RDDs are the building blocks of Spark and what make it so powerful: they are stored in memory for fast processing. RDDs are broken down into partitions (blocks) of data, a logical piece of distributed dataset. The underlying abstraction for blocks in Spark is a ByteBuffer, which limits the size of the block to 2 … Continue reading Spark Error “java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE” in Spark 1.6