In this contributed article, Ozan Unlu, CEO and Founder of Edge Delta, explores how a cloud-first world demands that observability be approached in a different way, one that favors "small data" over "Big Data." In some cases, Ozan believes, a central repository is no longer even needed.
What is the shift from Big Data to Small Data in observability?
Organizations are shifting from Big Data to Small Data in observability because the volume of data generated has become overwhelming, making it difficult to extract meaningful insights. The traditional 'centralize and analyze' method is no longer effective, as it can lead to clogged data pipelines and increased storage costs. By focusing on Small Data, organizations can analyze data at the source, reducing blind spots and improving real-time analytics.
How does analyzing data at the source benefit organizations?
Analyzing data at the source allows organizations to maintain oversight of all their data while minimizing storage costs. This approach helps avoid the indiscriminate discarding of potentially valuable data, reduces the risk of blind spots, and enables quicker identification of anomalies. Additionally, it eases the pressure on data pipelines, allowing for more agile and efficient real-time data analytics.
What role does accessibility play in modern observability?
Data accessibility is crucial for IT teams as it ensures that developers can quickly access the datasets they need, regardless of where they are stored. This eliminates the delays associated with relying on operations team members to provide access, fostering a more efficient workflow. In a landscape where data volumes are surging, having streamlined access to both small and large datasets is essential for effective observability and decision-making.
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