How to Simplify Real-Time Data Processing from Kafka for Better Decision Making
Easily process complex data streams with IDMC’s Advanced Data Integration Services and the Informatica Kafka Connector
In today’s competitive digital economy, you must be agile and data-driven to identify new business opportunities, better serve customers and improve operations. One way you can accomplish this is by leveraging data from streaming and real-time sources for their analytics use cases.
To leverage the streaming data sources, companies are moving from batch processing to micro-batch processing, and if necessary, real-time processing. But data pipelines cannot be transformed into real-time overnight.
It’s a complicated process. To start, each pipeline needs to be evaluated to see if it’s cost effective or even necessary. Then entire business processes must be reengineered, and nightly batch processing must be replaced with real-time processing.
Because the processes are usually linked to countless systems designed for batch processing, this is a complex and time-consuming undertaking. Since most companies use Kafka for streaming data, one way around this is by using the Informatica Kafka connector, which helps simplify near real-time data processing without comprising advanced transformations, such as machine learning (ML) transformations, hierarchy processor transformation and others.
How the Informatica Kafka Connector Boosts Batch ETL Processing
When the Informatica Kafka connector is combined with Informatica Advanced Data Integration services, which are part of the Intelligent Data Management Cloud™ (IDMC), performance is optimized for your micro and macro batch extract, transform, load (ETL) use cases.
So why Apache Kafka? To start, it is extremely popular for its rich features, like robust real-time messaging system queue, scalability, durability, fault-tolerance, etc. This helps with handling a high volume of data and enables you to pass messages from one endpoint to another. Kafka is the de facto technology that developer and architect communities use to build scalable, real-time data streaming applications. In fact, Kafka is used by over 80% of the Fortune 100, including Twitter, Uber, Netflix and Spotify.
To process the Kafka data streams and handle unpredictable data workloads, you need a highly scalable tool and fewer dependencies on infrastructure management. To ensure data is delivered on time, a massively parallel processing engine, like Spark, will help ensure data does not get stuck in the queue.
That’s why IDMC’s Advanced Data Integration services use Spark as the execution engine for running a Kafka batch ETL job on a serverless infrastructure managed by Kubernetes. To help you scale efficiently, IDMC’s Advanced Data Integration services dynamically balance clusters up or down based on demand. The services also shut down clusters when jobs are done, which optimizes resources. These services can perform data integration on virtually any cloud with extract, load, transform (ELT), Spark or a fully managed serverless option. Let’s explore the capabilities of the Informatica Kafka connector with the help of a real-world use case.
Real-World Use Case of the Kafka Connector
Let’s say Company X, an e-consumer business, wants to better understand shopping and buying patterns of their customers. This requires a holistic view of the customer’s data, from social media engagements to financial transactions.
- To access this data, a complex data mapping is needed, which involves combining real-time structured or unstructured data and running advanced transformations.
- The curated data is efficiently loaded into a cloud data warehouse, like Snowflake, Amazon Redshift, Microsoft Azure, etc. This is then processed further to gather knowledge for a targeted marketing campaign.
- Company X uses IDMC’s Advanced Data Integration services to help design a single mapping logic for the whole data flow. This saves time for the team and improves visibility into the end-to-end data pipeline, so everyone has access to the same trusted and reliable data.
- With the help of the Informatica Kafka connector, you can quickly access the Kafka data store, where Company X’s raw data resides. This enables you to access the real-time data and modify it in fraction of second, all in one go.
- Company X generates a wide range of data types, like CSV, text delimited, XML, JSON, Excel, ORC, Parquet and Avro. The Informatica Intelligent Structure Discovery tool helps process these various data formats.
- Because it supports multiple communication modes, the Informatica Kafka connector protects this sensitive customer data with tools like one-way secure communication with simple authentication and full two-way SSL communication support.
- Once the Informatica Kafka connector successfully parses the source data, it gets fed to the data integration pipeline. From there, multiple Informatica transformations can be applied over the data to extract valuable information.
- To drill down for deeper predictions, Company X will likely run ML algorithms on ML models using MLOps. Doing so uncovers detailed insights on shopping patterns, which will help provide a seamless customer experience.
- Finally, the prediction/transformed data is pushed to a data warehouse target (Snowflake in this case), which allows an analyst to mine the report further and make business decisions accordingly.
IDMC’s Advanced Data Integration services can be a cost-effective strategy for Company X. The streaming application jobs are costly with on-demand clusters. With Kafka batch support, you can schedule the cluster across days, which saves time and increases productivity.
Below is an example of an advanced data integration mapping that illustrates a Kafka batch ETL pipeline. Figure 2 shows how:
- The Kafka connector is used to fetch the data from the Kafka sources.
- An ML transformation is applied for fine tuning the pattern.
- The valuable output is pushed to the Snowflake target.
When used with IDMC’s Advanced Data Integration services, the Informatica Kafka connecter helps simplify your day-to-day batch analytics use cases. This saves time and money and enables analysts to access standardized real-time data for better decision making. Learn more about the Informatica Kafka connector or sign up for free a cloud data integration 30-day trial today.
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