The modern business world is fueled by data. Across all industries, companies are increasingly investing in data management and data analytics to mine user insights critical to their operations. The global big data market is expected to double its current value to reach $655 billion by 2029.
Large-scale data management and analytics require tremendous amounts of processing and memory resources. However, those resources are not always optimally allocated and utilized. HTEC’s vast experience in working with large volumes of data in complex infrastructure environments has consistently revealed a wide variety of inefficiencies that waste valuable resources and slow down or impede efficient data processing.
Apache Spark is the analytics engine for large-scale data processing preferred by Fortune 500 companies. It is capable of processing large volumes of data at exceptional speeds, but in complex environments it too can suffer from efficiency and performance issues.
To counter these inefficiencies that hinder the data processing pipeline and can amount to tens of thousands of dollars in unnecessary expenses, HTEC’s Data Engineering team devised an approach to defining systematic automated solutions that can identify and optimize improper resource allocation.
In this whitepaper you will learn:
- What are the common causes of improper resource allocation and inefficiency issues in Spark-based big data ecosystems
- How these issues impact the overall data processing efficiency and its related costs
- What is HTEC’s approach to defining systemic automated solution for performance monitoring and optimization
- How these solutions impact the overall performance of Spark-based big data ecosystems
