Big data analytics workloads: Challenges and solutions

The number of analytics workloads has increased significantly in recent years since more organizations than ever before are collecting massive amounts of data from endless sources, and depending on the insights from this data to gain a competitive advantage.

These analytics include logs and event analysis, user behavior, IoT, statistical analysis, complex SQL, and data mining. Furthermore, new analytics methods have emerged recently, including Hadoop MapReduce, data lake architecture, data virtualization, partitioning, etc.

The biggest challenge is that most analytic workloads are unpredictable and require strict performance levels when executed. To meet these performance requirements, many data platform teams turn to the Enterprise Data Warehouse (EDW), which requires moving the data, as well as data preparation and modeling

What Are Big Data Analytics Workloads?

Big data analytics is the process of examining large, complex, and multi-dimensional data sets by using advanced analytic techniques. These data sets can include unstructured, structured, and semi-structured data from different sources and sizes.

The unique demand that analytic processing places on modern information processing systems are referred to as analytic workload. The systems assigned to handle the workload often experience robust design and deployment implications.

Big Data Analytic Workload Challenges

Before building, selecting, or deploying an analytic infrastructure, one needs to understand the fundamental challenges and requirements of an analytics workload.

Managing Large Volumes of Analytics Workloads

There is no specific threshold that makes a data set large; however, it’s fair to say that data volumes tend to be counted in TBs. Applications like web analytics, fraud detection, and decision support often involve petabytes of data. Metrics that increase data volume include

• Row numbers — large numbers of a table’s rows will increase analytic workload requirements. When analyzing billions of rows, any inefficiency or overhead cost becomes expensive
• Dimensionality — tables often contain hundreds of columns. Since larger rows consume more storage and processing space, the workload complexity increases as the columns increase.
• Redundant storage — the storage of indexes and other metadata is designed to simplify serial and selective data retrieval in Database Management System (DBMS).

Data Model Complexity

Large data volumes increase the need for streamlined and efficient processing. Combining large volumes with complex data structures can result in impractical processing demands. Big data typically includes several dimensions:

• Data object complexity — data representation is usually spread across multiple data objects. Those objects must be “joined” or combined at run time by the processing platform. Consequently, the resultant processing’s magnitude and complexity increase as the amount of relationships increases.
• Data diversity — analytic repositories often encounter many different data styles and types while ingesting data from alternate sources. The ingestion of data from multiple sources creates a spike of additional load on the processing system.

Complex Computation

Analytic processing often involves statistical analysis and additional advanced computational methods. Analytics systems apply a wide range of statistical and mathematical operations to extract patterns and insights from raw data. Computational complexity increases workloads on the server layer and the amount of work performed during a given query request.

Temporary Data Staging

Intermediate data sets and results of advanced modeling and analytic methods are moved to the staging area or caching layer by the analytic operations. Analytics systems that run these methods must have the ability to integrate, write, and retrieve intermediate data at high speeds and high volumes. These operations substantially increase the processing requirements of the related queries.

What are the Solutions?

Businesses can use data virtualization platforms with a smart indexing layer that runs directly on their data lake. These tools can improve analytics performance by providing visibility into workloads and access to controls. The tools can also provide automation at the workload level to optimize for price and performance.

More advanced virtualization solutions enable one to identify overall workload behavior by grouping queries by the user. By mapping out the resource utilization of entire workloads, businesses can prioritize resources and control costs per workload. The ideal query prioritization should map back to business priorities.

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