Many enterprises are ramping up their efforts to adopt new and emerging technologies that come bearing promises of meeting the increasing demands for agility, scalability, and innovation. Despite adoption, various complexities—including remaining legacy infrastructures, data silos, governance issues, and performance and latency obstacles—inhibit these technologies’ tangible success from coming to fruition.
Matan Libis, VP of product at SQream, Ashwin Rajeeva, co-founder and CTO at Acceldata, and Preeti Kodikal, director of product marketing at Acceldata, joined DBTA’s webinar, Building a Next-Generation Data Architecture: Key Capabilities and Strategies, to examine the ways that designing, implementing, integrating, and managing the systems that collect, store, process, and analyze data will successfully support the latest technologies that aim to bring your business to the next level.
Enterprise data is exploding, according to Libis; between massive data sets, rapidly changing data, cloud and on-prem environment spend, deployment inflexibility, time-consuming data prep, highly complex queries, and more, organizations have virtually no choice but to adopt a next-gen data architecture.
A paradigm shift in enterprise analytics, where GPUs and CPUs have radically changed in their capabilities and purposes, has led to the emergence of new data architectures. Armed now with an understanding of what is best suited for GPU or CPU workloads, enterprises should look toward capitalizing on this understanding with adaptable resource allocation and minimal RAM and I/O bottlenecks.
To do so, Libis introduced SQream’s GPU-based Big Data platform, a scalable platform engineered to harness the speed, power, and efficiency of supercomputing resources and apply them to data pipelines, data engineering, and machine learning. Offering hands-free optimization, adaptive compression, and ultra-fast throughput, SQream’s platform enables enterprises to truly transition to a next-gen data architecture, according to Libis.
Rajeeva and Kodikal focused their discussion on the role that data observability plays in regard to next-gen data architectures, arguing that though data products drive the enterprise, data teams ultimately lack visibility. According to “Gartner Innovation Insight: Data Observability Enables Proactive Data Quality,” data observability goes beyond traditional monitoring and detection; it also provides robust, integrated visibility over data and the data landscape.
This is especially relevant in the context of the AI era; while every enterprise wants to be data and AI-first, ensuring that their data is ready for AI/ML use cases is fundamentally intertwined with data observability, according to the speakers.
They explained that data is essential for all modern enterprises today, regardless of industry. Whether they want to innovate new products, enhance digital services, make smarter business decisions, or run AI and LLM use cases, all of these initiatives rely on trustworthy data flowing across an enterprise’s data systems. However, roadblocks such as complex pipelines and too many data sources, petabyte scale data, and the clash of legacy and modern data stacks present great challenges to cultivating an ecosystem of trustworthy, transparent data.
With Acceldata, organizations can build and manage data products at scale by ensuring reliability, eliminating operational blind spots, and reducing spend to achieve high ROI on their data investments. As an all-in-one enterprise data observability platform for cloud, on-prem, or hybrid environments, Acceldata can help enterprises monitor, troubleshoot, and optimize their entire data stacks—from ingestion to consumption.
With one billion transactions daily and SOC2-Type and ISO 27001 certifications, Acceldata can provide:
- Data reliability throughout the entire modern data architecture
- Cost optimization across multiple data clouds and on-prem data architectures
- Pipeline performance enhancement across thousands of data pipelines throughout on-prem and cloud environments
- Reliable data, data pipelines, and data architecture that drive AI/ML readiness
For the full discussion about crafting next-generation data architectures, you can view an archived version of the webinar here.