Cloud infrastructure, elasticity and consumption models have greatly helped data practitioners solve the day-to-day challenges of analytics platforms. However, the struggles of data integration and turning data into meaningful and responsive insights continue to eat up a lot of engineering bandwidth. Integrating data from various sources with different schemas is either painfully slow, expensive, or labor-intensive. Add to this the need to replicate and analyze data from real-time streaming sources like Kafka and you have a big challenge.
With the need to copy data from various sources and generate holistic real-time insights, companies are now shifting their focus towards next-gen cloud data warehouses like Firebolt. Firebolt delivers sub-second, high concurrency analytics through innovations built on top of a de-coupled storage and compute architecture. Replicating data from Kafka to Firebolt transforms Kafka event streams into workable, structured data, which can then be used by developers, and data practitioners to generate business insights.
High-speed ETL platforms like Hevo simplify the process of shipping data from Kafka to Firebolt in as easy as four steps. Hevo and Firebolt carry out Kafka ETL with best-in-class execution speed and scalability that adapts to your workload needs.
Why Do You Need ETL for Your Kafka?
Fundamentally, Kafka is a streaming service that sends event data from A to B. It accepts all incoming messages, irrespective of their format, and makes them available for consumption. The event data is either semi-structured or unstructured and is not optimized for reading or querying.
If you are trying to run SQL queries on raw Kafka data, you are unlikely to get anything useful. Individual pieces of data don’t have value in and of themselves. To make sense of this data, you need to aggregate and transform the raw data (like JSON) into structured data so that can be queried and then loaded into your warehouse for further analysis.
Achieving this requires you to introduce a data store or a data warehouse as an intermediary stage between Kafka and the analytical systems.
This approach is advantageous since you can:
- Capitalize on Kafka data using cost-effective storage of your warehouse
- Introduce new use cases without worrying about performance and stability issues or hampering your production environment
- Ensure data quality and governance by working on a single repository
- Connect Kafka data in your warehouse to business intelligence and data analytics tools for decision support
Firebolt + Hevo - Addressing ETL for Kafka
The combination of Firebolt and Hevo is the Reese’s Peanut Buttercup for data engineers. You get sub-second analytics and a no-code data pipeline to address your Kafka use cases.
A data warehouse is core to your data management strategy. It provides a 360-degree view of your business by gathering data from different sources like Kafka, integrating, transforming, and refining it to provide valuable business insights.
Firebolt provides rapid warehousing with the best price-performance ratio in the industry. For a Kafka user, Firebolt is a quantum leap to deliver combined improvements of speed, scale, and efficiency with faster and better data analytics.
For cases where you need to replicate large amounts of data from Kafka, Firebolt is the ideal fit, providing ingestion at scale with low-latency visibility. Once ingested, data is stored in Firebolt’s native ‘F3’ (aka Triple ‘F’) file format resulting in average 10X data compression. ‘F3’ uses a sorted, indexed, compressed format enabling sub-second analytics.
Additionally, Firebolt offers native support for semi-structured data. It is possible to store semi-structured data (JSON or XML messages) in a nested array structure and perform any combination of flattening using SQL. Flattening introduces Firebolt unique indexes that enhance query performance.
Firebolt also offers a set of Lambda-style native array functions that you can use in SQL to query Kafka's semi-structured data. Because these array functions are invoked based on the schema, the Firebolt query engine does not have to perform full scans to traverse the underlying nested structure. You get sub-second performance without the need for preprocessing or the provision of RAM-heavy nodes.
Complementing the Firebolt data warehouse, Hevo does the heavy lift on the data integration front. Hevo is an Automated, No-Code ETL solution that supports 150+ ready-to-use integrations like Apache Kafka, Kafka Confluent Cloud, databases, SaaS applications, cloud storage, SDKs, and streaming services to replicate your data into Firebolt.
With Hevo’s in-built transformation capability, you can prepare your data in Firebolt analysis ready. Once the data is loaded into Firebolt, you can run Hevo’s models and workflows for post load transformation. You can even transform your data before loading in Firebolt by using either Python-based or drag-and-drop transformations.
Hevo's highly intuitive interface allows you to create a Data Pipeline with only a few clicks. Your teams don’t need any extensive training or technical resources to set up and use Hevo; even non-data professionals can set up their own Data Pipelines seamlessly.
Key Benefits of Using Firebolt + Hevo
- Blazing Fast Setup: As a SaaS offering Firebolt eliminates infrastructure management. Bring your data sets and you are off to the races. Similarly, Hevo Pipelines can be set up in minutes and have faster loading times, as they load data directly from Kafka to Firebolt.
- Built To Scale: Firebolt leverages de-coupled storage and compute with unlimited scalability of S3 and choice of instance types. Firebolt Engines can scale-out, scale-up, scale-in or scale-to-zero. Hevo scales horizontally to handle millions of records per minute with very little latency. This ensures the long-term viability of your business.
- Built for Data Engineers and Developers: Firebolt leverages SQL and REST APIS, helping Data Engineers with automation and SQL based sub-second analytics. Hevo’s capabilities for data formatting and transformation can automatically prepare your data for analysis in minutes.
How to Set Up Your Data Analytics Stack with Kafka, Hevo, and Firebolt
Using Hevo & Firebolt as your ingestion and storage layer, you establish a holistic data analytics stack that creates an environment for strategic decision-making, data analytics, business intelligence (BI), and data mining.
Hevo offers flexible data replication options that allow you to choose the type and amount of data you want to consume from Kafka. It provides visibility into various aspects of data replication such as filtered views, ingestion latency and speed, historical use details, and gives you the ability to recover from any errors.
Hevo supports both variations of Kafka: Apache Kafka, the open-source distribution, and Kafka Confluent Cloud, the managed service via Confluent.
All it takes is 4 simple steps to set up your Kafka ETL with Hevo.
You can start data replication from Apache Kafka, the open-source distribution, to Firebolt in a matter of minutes by
- Step 1: Configuring your Apache Kafka source
- Step 2: Selecting objects for replication
- Step 3: Configuring Firebolt destination
- Step 4: Reviewing settings
Once your Kafka Pipeline is set up, Hevo will acquire fresh and updated data from your Kafka cluster every five minutes (default pipeline frequency) and replicate it into Firebolt (incremental load). Depending on your needs, you can also customize the pipeline frequency from 5 minutes to an hour.
Note: Hevo only supports JSON Format for Kafka.
After your data has been replicated into Firebolt, you may connect to reporting and dashboard workloads like Looker, Tableau, Superset, Metabase, etc. on top of Firebolt to gain business insights. These dashboards serve as the third and final layer of your data analytics stack to create useful visualization of your data.
Use Hevo & Firebolt to Jumpstart Your Analytics Today
For first-timers, creating Kafka ETL pipelines to Firebolt might seem like a daunting task. The new pace of decision-making and changing customer expectations requires a fundamental shift in the way your business captures and stores Kafka data.
Using Hevo & Firebolt you can easily create and deploy a holistic data analytics stack that is secure, scalable, and fault-tolerant to handle all your data workloads. This combined architecture of continuous ingestion and storage creates a solid groundwork of seamless integration and fast analytical processing that is bound to serve you well in the long run.
Start your journey with Hevo and Firebolt today and experience an entirely automated, hassle-free data replication for Kafka. Hevo’s free trial gives you unlimited free sources and models to choose from, with support for up to 1 million Kafka events per month, and their website offers a great live chat service backed by an amazing 24/7 support team to help you get started.
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