Due to the exponential growth of data globally, your business is probably receiving tons of data from IT equipment, sensors, smartphones, websites, search engines, and other non-traditional sources. Processing vast amounts of data in real-time to improve your business’s operations and serve your customers better requires building scalable cost-efficient data pipelines. These five tips should help you.
Provide for rapid experimentation
Scalable data pipelines will not be cost-efficient if they don’t offer the ability to try new models and datasets. This ability is critical because it allows you to reduce the time taken to acquire and prepare new data sources to combine with existing data sets. For instance, you could turn to pre-packaged connectors to familiar data sources so you can experiment with different data sets without writing any code.
Rapid experimentation also needs helpful tools that can develop data pipelines using editable, automatically generated code. For example, you could have a code for building your business logic and connecting to the data through a UI driven interface.
If you want to streamline data cluster management, you have to invest in Spark Structured Streaming pipelines. The costs of maintaining the channels can spiral out of control since they are long-running. One sure way to keep the costs down is to apply automation but still maintain the business SLAs.
You could make full use of the spot and preemptible node types as they allow you to make your workloads more cost-effective. But knowing when to scale them up or down is a guessing game for data engineers who often oscillate between under-provisioning and over-provisioning to find what works for them. Automation takes the guesswork out of the game by adjusting cluster sizes based on user patterns.
Allow for cloud portability
Gardner’s survey revealed that more than 80% of respondents use more than one public cloud provider. This means that you are generating and storing data in multiple clouds. It is vital to have a strategy that doesn’t lock in a particular storage format, user interface, data processing framework, or repository.
This strategy helps you find an available technology that provides the same pipeline capabilities for every public cloud you intend to use. Data engineers can make things easier by recreating an existing pipeline in each cloud without rewriting their code.
Easy testing and debugging
Data pipeline scalability would be challenging achieving if the testing and debugging processes are not easy to institute. Since scalable data pipelines require iterative and acyclical methods, you must have reliable ways to test and debug them. A dry run lasting a few minutes using a subset of the input data is often adequate. It will help you verify connectivity and ensuring that the data schema is correct. It is also an implication that business logic is complete and can perform as expected.
Ensure data accuracy and consistency
Long-running data pipelines can run into issues with accuracy and consistency due to data input from multiple sources. When building scalable and cost-efficient data pipelines, have a close look at the data transfer process. Be sure to also manage the pipeline updates. Also, establish predefined, regular control points for data once it hits the data lake. It is a sure way to have ordered, reliable, and error-checked data.
Making sense of the vast amounts of data your business receives can be quite challenging yet meaningful. Data pipeline scalability can be overwhelming if you don’t know how to use it to streamline your business activities. Helios can help you build scalable and cost-efficient data pipelines to propel your company to new heights. Contact us today.