How to Overcome the 4 Biggest Challenges in Big Data Workload Optimization
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This article was submitted and written by Intel.
Big Data workloads are becoming more prevalent among enterprises and digital native businesses, yet remain inordinately expensive. Despite the growing popularity of batch processing, stream processing, and interactive/Ad-Hoc analytics, there are few optimization solutions available that take a holistic and automated approach to the challenges that these companies face.
In this guide, you will learn how to overcome the four primary challenges that companies face when it comes to optimizing their Big Data workloads.
Big Data workloads are complicated, with multiple layers of varying technologies and platforms required to function. Optimization of these workloads becomes quite challenging, even for the most knowledgeable of data engineers.
There are so many aspects that data engineers need to consider in order for their Big Data workloads to operate effectively. To begin with, they need to consider the following:
- Data Storage: How to store data in a way that is both cost-effective and efficient for processing. This may involve using data storage solutions such as a data lake.
- Data Formats: The format in which data is stored can also have an impact on processing efficiency. For example, using a columnar storage format like Parquet can be more efficient for analytical workloads than using a row-based format like CSV.
- Data Partitioning: Partitioning data can make it more efficient to process large amounts of data. For example, partitioning data by date can make it more efficient to run queries on a specific date range.
- Data Compression: How to compress data in order to save storage space and reduce the amount of data that needs to be processed. Different types of data may require different compression techniques.
- Data Cleaning and Preprocessing: How to clean and preprocess data in order to make it ready for analysis. This may involve removing duplicate records, handling missing values, and transforming data into the correct format.
- Data Processing Framework: Choosing which data processing framework to use, like Apache Hadoop MapReduce and Apache Spark, both open-source big data processing frameworks
- Data Security: Using encryption and access control, to protect sensitive data and comply with industry regulations.
With so many elements to consider, there are bound to be inefficiencies and those can add up to slower job completion time and higher costs.
Lack of visibility into big data workload performance harms data processing strategies because it makes it difficult to optimize the performance of data processing tasks and identify bottlenecks in the workflow.
If data teams want to optimize performance, they need access to real-time monitoring, to collect, store and visualize metrics like job completion time, CPU and memory usage. They also need to consider how to log data pipeline events and keep track of how changes to the data set are affecting the infrastructure.
Currently, teams need to combine a number of visibility tools, to have a 360 degree view of what is going on in their Big Data activities and to keep on top of all the changes in real time.
High volumes of workloads are hard to optimize, especially when they're constantly scaling up and down. Data engineers have to consider the challenges of handling large volumes of data in a variety of formats and at high speeds, managing data consistency and partitioning in distributed systems, and addressing issues of data quality, security and compliance as the data scale.
Additionally, it's important to consider the infrastructure cost and limitations of hardware and network, and the need to optimize the use of resources such as compute, storage, and networking to handle data processing at scale.
Even minor changes in workloads can hurt cluster performance and require retuning of code and configuration. Data engineers have to consider the challenges of handling the dynamic nature of big data workloads which includes dealing with changing data structures and formats, handling sudden spikes or drops in data volume, and adapting to evolving requirements and use cases. This requires the ability to quickly adapt and change the data pipeline, and to handle a high degree of variability and unpredictability in terms of data volumes, data sources, and data processing requirements.
Data engineers also have to be able to scale up and down the infrastructure in a flexible way to match the dynamic nature of big data workloads, this may involve the use of cloud computing resources, containers or serverless computing. Currently, data engineering teams have a "set it and forget it" mentality when it comes to workload configuration, which means that as pipelines, volumes and sources inevitably change, they must make changes manually.
By operating autonomously and continuously, Granulate optimizations produce more efficient workloads no matter how complex the environment. This is especially relevant considering that Granulate is infrastructure agnostic and able to optimize on all of the most popular execution engines (Kafka, Spark, Tez and MapReduce), platforms (Dataproc, Amazon EMR, HDInsight, Cloudera and Databricks) and resource orchestrations (YARN, Kubernetes and Mesos).
When it comes to visibility, the Granulate dashboard enables a full view of your data workload performance, resource utilization and costs. The dashboard gives full visibility of all Granulate data processing optimization activities and the ability to deploy, monitor and adjust your agents as needed.
With continuous optimization, Granulate ensures that workloads remain efficient, even when scaling rapidly. As data volume, variety, velocity and veracity fluctuate, the Granulate agent is constantly updating to ensure that resources are allocated efficiently, with minimal CPU and memory wasted.
Granulate makes it so that data engineering teams don't have to spend their time making manual changes by effectively optimizing data workloads despite their dynamic nature. Data pipelines are constantly changing, so an autonomous, continuous solution is almost a necessity for reducing compute costs.
Using Granulate, applications run more efficiently, minimize CPU and memory resources, reduce time to completion, and lower costs. Companies have saved as much as 45% on data processing costs by deploying Granulate's agent which continuously optimizes application runtime and resource allocation within the workload.
Granulate's approach to Big Data optimization works on two levels at the same time. On the runtime level, Granulate applies the most efficient crypto and compression acceleration libraries, memory arenas Profile-Guided Optimization (PGO) and JVM runtime optimizations. At the same time, Granulate tunes YARN resource allocation based on CPU and memory utilization, optimizes Spark executor dynamic allocation based on job patterns and predictive idle heuristics and optimizes the cluster autoscaler.