By Steve Sarsfield, Developer Advocate at Ocient
The term “big data” has been around for over two decades, but its challenges have evolved. Early technologies just don’t cut it anymore. Today’s enterprise data environments demand advanced solutions to manage, process, and analyze vast amounts of data. This post looks into the new challenges in dealing with big data and the cutting-edge technologies that help enterprises tackle them.
Research has led us to believe that big data challenges are evolving, and so must your big data solutions. Our recent report, Beyond Big Data: Reaching New Altitudes, showed that many companies are thinking about the sometimes-opposing forces of data growth and sustainability, as well as the tradeoffs between performance and cost. The next generation of solutions will have to take those challenges head-on and offer new ways to meet organizational needs.
Meeting the Challenges of Modern Big Data
Whether your company deals with terabytes or petabytes of data, today’s challenges require modern solutions capable of handling the complexities of rapidly growing data volumes. Consider the following factors as you assess your organization’s ability to manage big data:
- Handling Data Growth: It should be obvious, but the exponential growth we all face means you need scalable solutions. Legacy systems struggle to meet the demand for faster storage, processing, and real-time insights. With legacy systems, organizations often find themselves paying too much in infrastructure costs to handle big data, using data subsets that lead to incomplete analysis, or missing their analytics time windows. Yet, there will always be growth in data—today, tomorrow, and months from now.
- Providing Both Batch and Streaming: As businesses demand more real-time data for decision-making, traditional systems often fall short. Combining real-time data feeds with batch data sets requires optimized pipelines that can process data instantly. The need for speed becomes even more critical with data’s growing volume and velocity.
- No Fuss Data Transformation and Data Quality: Modern data pipelines often rely on Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes. With large-scale data, both approaches introduce performance bottlenecks that lead to missed analytics windows. If the data takes too long during ETL, business intelligence is too dated. Modern analytical engines must be designed to efficiently manage data quality, transformation, and loading.
- Handling Data Silos: As data volumes continue to grow and the variety of data expands—including structured, semi-structured, and unstructured formats—effective data orchestration becomes increasingly important. Data orchestration, managing and coordinating data workflows across multiple systems and sources, enables organizations to automate data flow between diverse systems. Data professionals tend to do extra legwork when dealing with diverse data sources, and it’s best to simplify this process as much as possible, avoiding the potential pitfall of bringing in multiple tools to get orchestration done.
- Dealing with Concurrency: The ability to handle high concurrency is crucial in today’s data environments. With hundreds or thousands of users executing queries simultaneously, traditional systems may experience significant slowdowns. New solutions must be capable of dynamically adjusting resources to ensure real-time analysis without sacrificing performance, regardless of query volume.
- Meeting Service-Level Agreements (SLAs): Businesses expect near real-time insights, pushing traditional infrastructure to its limits. Modern analytical engines and hardware optimized for scalability allow organizations to meet tight SLAs with consistent and reliable performance.
- Optimizing for Cost and Performance: Handling large data volumes also comes with cost challenges. While many solutions can manage the data load, they often come at a steep price. The focus should be on implementing efficient data management practices that maximize performance without inflating costs.
The New Technologies of Modern Big Data
At Ocient, our goal is to enable companies to handle more data with less infrastructure while ensuring faster and more reliable insights. Here are some of our key technological advancements:
- NVMe Solid State Drives: One key challenge in large-scale data analytics is overcoming network bottlenecks that occur when data is stored far from the compute resources that process it. Legacy disk drives have improved over the years, but they can’t match modern technologies like SSDs. When attached via a fast interface like a PCI bus, SSDs can improve data access performance by up to 100 times versus traditional HDD. New architectures like Ocient’s Compute Adjacent Storage Architecture® (CASA) use NVMe SSDs minimize data transfer times, remove network congestion, and enable rapid data access. By eliminating these bottlenecks, organizations can process vast datasets in real time, improving the speed and efficiency of analytics tasks.
- Advanced Indexing for Real-Time Query Performance: Indexing is a fundamental component of any data analytics platform, as it allows for faster data retrieval. Rather than needing to add more computing power to speed up access, how about adding optimizations like indexing to get more from the same hardware? Ocient has a comprehensive set of advanced indexes—such as clustering, inverted, hash, n-gram, and geospatial indexing—that are designed to accelerate query performance even on large, complex datasets.
- Focus on Cost and Energy-Efficient Processors: With data centers consuming vast amounts of energy, selecting and optimizing energy-efficient processors has become a key priority in the big data space. Modern analytics platforms like Ocient now focus on selecting the most energy-efficient processors and ensuring they are fully utilized through intelligent workload distribution. Make it a priority to focus on technology that uses less energy while delivering fast analytics.
- Parallel Task Execution for High Throughput: To meet the growing demand for real-time analytics, modern systems must be capable of simultaneously processing an immense number of tasks. Technologies like Ocient’s Megalane™ enable parallel task execution and allow systems to run an extreme number of concurrent tasks without slowing down. This capability is critical for organizations that need to analyze vast datasets or perform complex queries in real time. By enabling higher throughput, Ocient can deliver insights faster, keeping pace with the speed of modern business needs.
Addressing Evolving Needs with Ocient
As big data environments evolve, even companies not at the top tier of the data scale need to adopt modern, high-performance analytical engines. We built Ocient to deliver:
- A More Complete Solution: Nowadays, there shouldn’t be a need for separate ETL/ELT, orchestration, streaming, and batch processing. Licensing and managing best-of-breed solutions is costly, both in the licensing fees you pay and the expertise you need to manage them all.
- A Solution with a Smaller Carbon Footprint: Ocient optimizes resource usage and lowers the need for additional hardware and reduces energy consumption, contributing to green IT initiatives and lowering costs.
- A Solution with Performance Strategies: Businesses can accelerate decision-making by achieving faster query speeds on both large and small datasets. However, query performance should be tunable to meet demand within constrained resources such as capped number of servers.
- Workload Management: Balancing high-performance daily operations with scheduled reporting is crucial in large implementations. For example, a financial company may generate complex reports over the weekend, while ad hoc dashboards and security event logs require immediate real-time analysis. With Ocient, you can shift data gears between different service level tiers, improving performance and reducing costs.
By leveraging new technologies, businesses can future-proof their data strategies while remaining efficient and competitive.
Ocient: Built for the Future of Big Data
Ocient is at the forefront of enabling the next generation of big data analysis. Its data analytics solutions provide always-on, compute-intensive analysis that delivers up to 80% in price savings. Designed to run on the latest hardware, including NVMe SSDs, Ocient’s platform integrates data transformation, complex query processing, and AI into a single, optimized solution. The ability to deploy on-premises, in OcientCloud®, or in the public cloud means enterprises can scale without resource-intensive integration, preparing for the future of data.
To learn more about how Ocient can help your organization prepare for the future, contact sales.