The value of data can be maximised when it is used without delay. With Apache Flink, we can build streaming analytics applications that incorporate the latest events with low latency. In this lab, we will create a Pyflink application that writes accumulated taxi rides data into an OpenSearch cluster. It aggregates the number of trips/passengers and trip durations by vendor ID for a window of 5 seconds. The data is then used to create a chart that monitors the status of taxi rides in the OpenSearch Dashboard.

In this lab, we will create a Pyflink application that exports Kafka topic messages into a S3 bucket. The app enriches the records by adding a new column using a user defined function and writes them via the FileSystem SQL connector. This allows us to achieve a simpler architecture compared to the original lab where the records are sent into Amazon Kinesis Data Firehose, enriched by a separate Lambda function and written to a S3 bucket afterwards. While the records are being written to the S3 bucket, a Glue table will be created to query them on Amazon Athena.

In this lab, we will create a Pyflink application that reads records from S3 and sends them into a Kafka topic. A custom pipeline Jar file will be created as the Kafka cluster is authenticated by IAM, and it will be demonstrated how to execute the app in a Flink cluster deployed on Docker as well as locally as a typical Python app. We can assume the S3 data is static metadata that needs to be joined into another stream, and this exercise can be useful for data enrichment.

Stream processing technology is becoming more and more popular with companies big and small because it provides superior solutions for many established use cases such as data analytics, ETL, and transactional applications, but also facilitates novel applications, software architectures, and business opportunities. Beginning with traditional data infrastructures and application/data development patterns, this post introduces stateful stream processing and demonstrates to what extent it can improve the traditional development patterns. A consulting company can partner with her clients on their journeys of adopting stateful stream processing, and it can bring huge opportunities. Those opportunities are summarised at the end.

In the previous post, we discussed how to develop a data pipeline from Apache Kafka into OpenSearch locally using Docker. The pipeline will be deployed on AWS using Amazon MSK, Amazon MSK Connect and Amazon OpenSearch Service using Terraform in this post. First the infrastructure will be deployed that covers a VPC, VPN server, MSK Cluster and OpenSearch domain. Then Kafka source and sink connectors will be deployed on MSK Connect, followed by performing quick data analysis.

In this lab, we will create a Kafka producer application using AWS Lambda, which sends fake taxi ride data into a Kafka topic on Amazon MSK. A configurable number of the producer Lambda function will be invoked by an Amazon EventBridge schedule rule. In this way we are able to generate test data concurrently based on the desired volume of messages.

Kafka Connect can be an effective tool to ingest data from Apache Kafka into OpenSearch. In this post, we will discuss how to develop a data pipeline from Apache Kafka into OpenSearch locally using Docker while the pipeline will be deployed on AWS in the next post. Fake impressions and clicks data will be pushed into Kafka topics using a Kafka source connector and those records will be ingested into OpenSearch indexes using a sink connector for near-real time analytics.

Building Apache Flink Applications in Java by Confluent is a course to introduce Apache Flink through a series of hands-on exercises. Utilising the Flink DataStream API, the course develops three Flink applications from ingesting source data into calculating usage statistics. As part of learning the Flink DataStream API in Pyflink, I converted the Java apps into Python equivalent while performing the course exercises in Pyflink. This post summarises the progress of the conversion and shows the final output.

I recently obtained the Certified Kubernetes Application Developer (CKAD) certification. It is for Kubernetes engineers, cloud engineers and other IT professionals responsible for building, deploying, and configuring cloud native applications with Kubernetes. In this post, I will summarise how I prepared for the exam by reviewing three online courses and two practice tests that I went through.

This series updates a real time analytics app based on Amazon Kinesis from an AWS workshop. Data is ingested from multiple sources into a Kafka cluster instead and Flink (Pyflink) apps are used extensively for data ingesting and processing. As an introduction, this post compares the original architecture with the new architecture, and the app will be implemented in subsequent posts.