Data velocity is escalating at unprecedented levels and forward-thinking enterprises have accelerated the adoption of big data solutions. Despite the current economic climate, investments in big data continue to grow.
At the same time, there are continuing concerns about complexity and costs. Frameworks have become incredibly complex, requiring more specialized knowledge and time-consuming management. Spending on big data solutions, especially in the cloud, requires more stringent cost controls and better allocation of resources. Organizations cannot afford the high cost of inefficiency.
Read this guide to learn about the current state of big data and the main challenges involved in optimizing resource allocation and managing costs. Also, explore several of the more popular infrastructure and execution engines, and best practices for optimizing data processing workloads of all kinds. Finally, we will present solutions for optimizing big data workloads and controlling costs.
The Big Data optimization guide includes the following, and more.
The State of Big Data
Discover the trends that are influencing Big Data and why its infrastructures contain the most expensive workloads for enterprises.
Getting to Know Big Data Architecture
Read this clear and straightforward breakdown of Big Data architecture, from executor to ETL pipeline and everything in between.
The Four Main Challenges of Big Data Optimization
Explore the challenges that can make it difficult to optimize costs when managing Big Data organizations.
Optimizing Big Data Environments
Dive deep into the optimization strategies for Amazon EMR, Spark, Databricks, Kafka, Hadoop, Kubernetes, and more.
Big Data Workloads with Autonomous Optimization
Find out how continuous, autonomous optimization fits into your data streaming and processing strategy.