What do you do if you're a data engineer facing the rising need for real-time data processing?

Evaluate the following points of the business need: > What happens if you don't have the process in real time: how does it impact your productivity or competitiveness? > How long do you need the data to be in the system when it is already in the system? > What are the data sources to be used? > Which business processes need to be in real time? The time is required to understand well to define a real-time processes!

Embrace emerging technologies such as stream processing frameworks like Apache Kafka or Apache Flink, invest in scalable infrastructure capable of handling real time data streams, and continuously refine algorithms and pipelines to optimize processing speed and efficiency, ensuring readiness to meet the rising demand for real-time data processing in data engineering projects. You can also consider the offerings by cloud service provides. Like Stream Analytics from Microsoft Azure

Assess Requirements: Understand specific needs like data volume, frequency, and latency requirements. Choose Tools: Select tools like Apache Kafka for ingestion, Apache Spark for processing, and Apache Flink for analytics. Design Architecture: Create an architecture supporting ingestion, processing, storage, and analytics layers. Implement Pipelines: Develop pipelines for ingestion, processing, and analysis, ensuring scalability and fault tolerance. Monitor and Optimize: Continuously monitor performance and optimize for efficiency and reliability. Ensure Security and Compliance: Implement security measures and ensure compliance with regulations like GDPR or CCPA.

Master Streaming Technologies: Familiarize yourself with streaming frameworks like Apache Kafka, Apache Flink, or Spark Streaming. These tools are essential for ingesting, processing, and analyzing real-time data streams. Learn Real-time Data Architectures: Understand real-time data architectures like Lambda architecture, Kappa architecture, or microservices-based architectures. Each has its strengths and weaknesses, so choose the one that best suits your specific needs.

Very rightly said about compatibility of the services if I talk about end to end Streaming Pipeline GCP, It will contain: GCS Buckets : Data Storage Area in Cloud / Extraction Pub/Sub : Orchestrate Streaming Data Capabilities Dataflow : Transformation Tool of Choice / Transformation Bigquery : Serverless Warehouse for Data / Load This is one of the Use case and it vary based on requirement / Data Volume / Velocity of Data for reaching at the Sink System.

For the streaming data, asynchronous calls are very important, To introduce async services within workflow. Cloud services such as Pub/Sub could be used. Pub/Sub provides topic and message architecture where real-time data will be consumed through topics and will be published. Streaming Data use case effectively use case comes with Spark as well so we can use services Databricks on those scenario.

Transformation tool of choice is very pivotal while selecting for streaming data, If we have data coming from Pub/Sub it can be integrated with Tool such as Dataflow where worker nodes could act as catalyst for streaming data at a steady rate for our Extraction and Loading of Data Dataflow provides Orchestration and Transformation via apache beam where through Java or Python SDK we can create our pipelines and the source and sink could be of the choice satisfying the need of the ask.

Scalability is achieved best on Cloud Based solution where Bigquery can act as a serverless warehouse where it has the ability to scale up and down based on the bqload jobs. bqload jobs make sure that end to end flow of sql file where it contains Pl/SQL block or sql based transformations are taken into account. Scalable is best achieved where Data is put inside a Data Lake or Data Warehouse and Bigquery can act as Data Lake or Data Warehouse.