Today, most companies would say their data volumes are exploding. As a result, such companies lack the ability to efficiently perform big data analytics, or the ability to quickly leverage large volumes of data for quick, actionable insights. This is due to four key challenges:
- The high cost of managing large data volumes: Storing, processing, and analyzing large datasets can be prohibitively expensive, particularly when using traditional on-premises or cloud-based solutions that rely on CPU-driven architectures. These expenses can quickly escalate, especially when organizations scale their data operations.
- Slow query-response times: The sheer size and complexity of datasets can translate to slow query response times, meaning insights are sometimes obsolete and unsuitable to use for business decisions. When companies need to take time-sensitive action, these delays can result in missed opportunities and mediocre outcomes.
- Complex data management: Companies often struggle to effectively unify and manage data from disparate sources and systems, and this complexity hinders the ability to extract meaningful analytics.
- Scalability: As data grows, so does the need for a scalable solution that can handle the increased workload without compromising performance. Traditional data processing tools often fall short in this area.
Working together, SQream and Denodo enable companies to overcome each of these challenges, so they can engage in big data analytics and gain the insights they need, when they need them.
