Businesses that don’t stay abreast of technology trends run the risk of quickly falling behind the competition. As the rate of digital transformation accelerates and companies continue to create and ingest data, they are realizing that managing large, complex data movements requires new tools and technologies to keep pace with data’s endless scale.

We’re seeing a transition from batch data processing techniques, which are no longer sufficient for businesses that want speed to insights. Instead, companies are using the power of streaming data. Otherwise known as event-driven architecture, streaming data is a software design model that is structured as a stream of distinct changes to the state of data.

Streaming data is gaining popularity because it allows businesses to convey changes to data in real time. This symbolizes a radical shift in how businesses transmit data between systems, and it gives a quintessential model for immediate API updates. Among available choices, I’ve found that the open-source streaming platform Apache Kafka is emerging as an industry favorite.

Taking Advantage Of Apache Kafka

Apache Kafka rapidly sends messages consisting of new or revised data from source systems to the systems or applications that take in the information. The platform provides organizations with a broad range of features, including:

High Throughput: Apache Kafka was designed to handle high volumes of data in short time periods, enabling an organization to send thousands of messages per second. The speed at which the platform can receive, process and send messages enables businesses to update information in real time.

Low Latency: It sends data within milliseconds through networks or systems.

Durability: It duplicates and houses data across distributed systems to avoid any lost or missing information. As a result, data is stored safely and competently in the platform indefinitely.

Reliability: With distributed, partitioned, replicated and fault-tolerant features, it duplicates data to guard against data loss and functions even in the event of component failures, making it extremely reliable.

Scalability: It can absorb additional work and volume while easily scaling with no data loss or downtime.

Extract, Transform And Load (ETL) Capabilities: It performs the work of a traditional ETL by pulling data from one or more sources, converting it to a unified format and loading it into a destination system.

However, while many companies are making the investment in streaming data and event-driven architectures, speed and scale can’t prevent poor data quality from proliferating in systems. In fact, quite the opposite. As data volumes grow, that multiplier also applies to data quality errors. Quickly, organizations will be unable to address data quality issues using current manual and data analyst-based methods. As the number of companies devoting substantial time, money and resources to streaming data initiatives increases, data integrity has to be a foundational component of any data strategy to deliver high-quality data that consumers trust.

Establishing Streaming Data Quality

As businesses continue to consume large amounts of data, they should place a greater emphasis on ensuring the accuracy and reliability of data quality. To create consistent, complete and relevant streaming data messaging, organizations should follow nine critical steps to ensure data integrity throughout the life cycle of streaming data.

1. Establish data quality rules at the source to confirm data integrity.

2. Conduct in-line checks to guarantee that the data complies with standards and is complete. Certify counts and amounts, and recognize patterns and threshold violations in real time.

3. Detect and eliminate duplicate messages.

4. Verify the timely arrival of all messages. Confirm that messages were aggregated and transformed correctly, and certify that the appropriate consumers received the messages.

5. Reconcile and validate all messages between producers and consumers to ensure that data hasn’t been altered, lost or corrupted.

6. Conduct data quality checks to confirm expected data quality levels, completeness and conformity.

7. Monitor data streams for expected message volumes and set thresholds.

8. Establish workflows to route potential issues for investigation and resolution.

9. Monitor timeliness to identify issues and ensure service-level agreement (SLA) compliance.

The power and potential of streaming data to support the growing volumes of information and improve operations industrywide is driving businesses to event-driven architectures en masse. Automated data quality capabilities are necessary for companies to adopt before implementing messaging platforms because traditional, manual-based data integrity checks will fall significantly short in a streaming data world.