Before I joined Acceldata as the company’s first Chief Product Officer, I did a thorough examination of the Acceldata platform's data observability capabilities. To ensure its readiness for enterprise deployment, I spent considerable time with Ashwin Rajeeva, our CTO and co-founder, who offered detailed insights into the platform's architecture, emphasizing its limitless scalability. I was immediately impressed, but as someone who’s made a career in technology, I know that it's one thing to understand the mechanics, and it's quite another to witness it in action.
It’s now quite clear that the capabilities of the Acceldata platform are delivering beyond what was promised. I say this on the heels of a remarkable achievement - our platform has surpassed the 0.5 exabyte mark in monthly data observations. The sheer magnitude of an exabyte is awe-inspiring; to put it into perspective, it's one quintillion bytes, which is equivalent to 1,024 petabytes, 1,048,576 terabytes, or 1,073,741,824 gigabytes.
Let’s consider what this achievement of a half of an exabytes observed monthly signifies as a milestone. And we need to think about the impact of all this data on the world's largest Global 2000 enterprises that rely on the Acceldata platform day in and day out.
Enterprises have a growing demand for dependable and actionable data. This data is their foundation, and delivers valuable insights from analytics. It does everything from automate essential business processes to fulfilling compliance reporting requirements. As generative AI becomes a more essential element of enterprise technology and business strategies, data helps democratize how data is accessed and used.
But before they can operate with data, they have to trust that the data they’re using is accurate, timely, and usable.
A significant number of these enterprises are opting to replace outdated data quality tools in favor of a "shift left" approach to data reliability. This proactive strategy involves identifying and resolving issues before they have a chance to escalate, ultimately minimizing downtime, reducing unnecessary costs, and optimizing productivity. It prioritizes the prevention of data quality, integrity, and performance issues at the earliest possible stages, rather than treating them as an afterthought or addressing them only in later stages. The ultimate aim is to detect and rectify these issues as early as possible, thereby reducing the likelihood of their downstream propagation.
Now, shifting left is not a one-size-fits-all proposition. A platform must be capable of handling data at massive scale if data reliability efforts are going to truly be effective, and must be able to address these issues concurrently:
To effectively implement a "shifting left" strategy, a platform needs to process and analyze data at massive scale. This is because it must be able to detect anomalies, data quality issues, or performance bottlenecks as early as possible in the data pipeline. Detecting these issues in small-scale environments might not provide a comprehensive view of potential problems, leading to false positives or missed issues.
Enterprises generate vast amounts of data from various sources, and this data needs to be comprehensively monitored. A platform handling data at massive scale can cover a broader spectrum of data sources, ensuring that data quality and performance issues are addressed across the entire data landscape, not just in isolated parts of it.
Large-scale data environments often behave differently than smaller ones. Having a platform capable of processing massive data volumes allows for realistic testing of data quality and observability practices under conditions that more closely resemble the production environment. This is crucial to identify issues that may only arise at scale.
By handling data at scale, a platform can process and analyze data in real-time or near-real-time. This enables timely issue resolution, reducing the chances of problems escalating and causing significant downtime or financial losses. In large enterprises, even short periods of downtime or data quality issues can result in substantial costs.
As data volumes continue to grow rapidly with the rise of IoT, big data, and other data-intensive technologies, a platform designed for massive scale ensures that it remains relevant and effective in handling the ever-increasing data demands of the future.
In addition to data reliability, enterprises are equally focused on cost reduction. Spend intelligence and compute optimization within Acceldata's Data Observability platform represents an opportunity for enterprises to uncover significant ways to reduce costs by identifying overspending in cloud environments, opportunities to allocate costs by organizational unit, and other ways to optimize their operational costs.
By offering insights into compute, data, and tool usage, including popular products like Snowflake and Databricks, this platform empowers companies to redirect saved resources toward new initiatives. This ability to achieve substantial cost savings without compromising performance or reliability is the driving force behind why many large enterprises are giving top priority to enterprise data observability over various other initiatives.
In July 2017, CERN made headlines by revealing that its data center housed a substantial 200 petabytes in its tape library. Fast forward to today, and we're witnessing an astounding growth, with us observing over 2.5 times that amount every single month.
According to IDC's projections, by the year 2025, the world will be grappling with a mind-boggling 175 zettabytes of data, which equates to roughly 175,000,000 petabytes, all in need of storage solutions. As the internet of things, big data analytics, and artificial intelligence continue their ascent, the data landscape is poised to become even more colossal. We stand ready to embrace this data-driven future.
However, amid this data deluge, all enterprises recognize the need to ensure data quality in a way that aligns with their growth. With such vast volumes of information being generated and utilized, maintaining the integrity, accuracy, and reliability of data is critical.