3 reasons why you need data observability for your streaming data pipelines

As businesses race to meet consumer demands, real-time data has become a critical factor for success. The rise of fast commerce, characterized by rapid order fulfillment and delivery, has further amplified the need for businesses to harness the power of streaming data.

By using real-time insights, businesses can optimize inventory management, personalize customer experiences, detect and prevent fraud and make data-driven decisions that enhance growth and efficiency. Combining real-time data with agile development methodologies accelerates time-to-market, provides a first-mover advantage and helps capture market share.

Despite its potential for enabling real-time insights and decision-making, streaming data is often viewed skeptically within enterprises. According to IDC, it remains one of the least trusted data sources due to its susceptibility to errors, inconsistencies and incomplete information, especially when sourced from diverse and unreliable sources.

Data observability plays a crucial role in building trust by providing a comprehensive view of the data pipeline. It enables organizations to monitor data quality, detect anomalies and optimize performance proactively. Moreover, it empowers teams to adopt an agile, iterative approach to data engineering, fostering continuous improvement and innovation. Let’s explore this in detail.

Ensuring data quality in real time is paramount. The relatively nascent field of streaming data lacks standardized practices and methodologies. This can lead to inconsistencies in data processing, analysis and interpretation, further eroding trust in the data.

Streaming data often involves sensitive information, making it a prime target for cyberattacks. Ensuring the security and privacy of streaming data requires robust, resource-intensive measures. Data engineers must implement thorough data validation, cleansing and quality checks to maintain data integrity and reliability.

Data observability empowers organizations to proactively safeguard the quality of their streaming data pipelines. By continuously monitoring key data quality metrics, organizations can identify and address issues early.

Schema validation, along with checks for completeness, consistency and uniqueness helps maintain data integrity. Also, range and timeliness checks help ensure data accuracy and freshness. These data quality measures improve the reliability and trustworthiness of streaming data.

To help ensure governance of streaming data pipelines, data observability efforts can focus on data lineage tracking, access control and security, compliance with regulations and governance policies. By tracking the origin and transformation of data elements, implementing granular access controls, adhering to data privacy regulations and enforcing data quality standards, organizations can strengthen the security, privacy and overall governance of their streaming data pipelines.

Data teams working with streaming data pipelines must focus on continuously monitoring high-volume, unpredictable data loads. According to IDC, the global event stream processing market experienced a 21% compound annual growth rate (CAGR) from 2017–2022, with a projected 30% CAGR from 2022–2027.

Unlike traditional batch processing, streaming data flows constantly, making it vulnerable to a myriad of issues such as data quality problems, performance bottlenecks and security breaches. Implementing robust, automated monitoring solutions enables data engineers to proactively identify and address these issues before they escalate, helping to ensure pipeline reliability and data integrity. This proactive approach saves time and resources while preventing potential business disruptions and data loss.

Real-time alerting and anomaly detection are also crucial for maintaining the health of streaming data pipelines. Timely alerts for critical events such as data quality issues, pipeline failures or security breaches enable quick responses to mitigate potential risks. Anomaly detection techniques can help identify unusual patterns or deviations from expected behavior, enabling early intervention and preventing data-driven errors.

The iterative nature of streaming data pipelines needs a flexible and agile approach to observability. Data engineers must be comfortable with rapid prototyping, continuous testing, monitoring and fine-tuning pipelines. By continuously monitoring the data pipeline, data engineers can identify changes in data structures, formats or distributions.

This proactive approach allows for timely adjustments to the pipeline, helping to ensure data quality and reliability. By embracing an iterative mindset, teams can adapt to evolving data sources, refine data transformations and maintain data consistency, ultimately safeguarding the integrity of the entire data pipeline.

Continuous monitoring of data pipeline performance metrics allows for timely adjustments to resource allocation, query optimization and infrastructure scaling. An agile approach enables rapid experimentation with different technologies and configurations, leading to cost-effective solutions. By prioritizing data-driven decision-making and a continuous improvement mindset, organizations can significantly enhance the performance and cost-efficiency of their data pipelines.

IBM® Databand® monitors data pipelines, detects anomalies, alerts users to data incidents and helps remediate issues. It is designed to work with any data orchestration, data integration or workflow automation tools you might be using, bringing all monitoring data under one roof.

This unified approach enables users to manage all their data monitoring, alerting and quality assurance for both streaming and batch processing on a single platform. It helps data teams gain a deeper understanding of how their entire system operates and how automation and data pipeline tools are performing.

IBM Databand is designed to make streaming data pipelines more reliable, consistent and efficient. Its capabilities include:

• Building historical baselines to detect early anomalies: Gain real-time visibility into workflows to identify potential issues before they affect downstream processes.
• Monitoring data drifts continuously: Detect changes in data distribution, schema or quality to proactively adjust the pipeline and help ensure data integrity.
• Optimizing the cost and performance of extract, transform, load (ETL) and extract, load, transform (ELT) processes: Gain deep insights into performance metrics, enabling you to proactively optimize your workflows.
• Detecting and resolving data quality incidents quickly: Enhance data quality to drive better business value through proactive visibility into data.

We are getting into an era where real-time data cuts across all areas of business. Companies need to build, run, manage and monitor streaming data pipelines at scale, across environments. To deliver on this goal, IBM recently acquired StreamSets, a leading real-time data integration solution specializing in streaming structured, unstructured, and semi-structured data across hybrid multicloud environments. StreamSets when paired with IBM Databand provides best in the class continuous data observability of streaming data pipelines.

For more information, visit IBM Databand.