Big Data Benchmark: definition and top five best tools

The Big Data Benchmark tools allow you to evaluate and compare different Big Data systems. Find out what these tools are used for and a selection of the best of them.

In order to know whether a Big Data system will perform well enough to meet the needs of the business it is necessary to evaluate and compare it. It’s called Benchmarking. This is done using specialized suites of tools called Benchmarks Big Data. Based on the principle of a test bench, analysts test each of the hardware and software functionalities offered by the suppliers.

What is the Benchmark?

These tool suites include micro benchmarks, component benchmarks and application benchmarks. First of all, Micro Benchmarks are used to evaluate low-level system operations. Component Benchmarks are used to evaluate high-level functions. Finally, Application Benchmarks measure the system for application performance.

Why do a Big Data Benchmark?

benchmark big data advantages

The Big Data Benchmark suites offer multiple benefits. In particular, they allow you to analyze the memory hierarchy, measure the intensity of operation, and characterize the workloads.

In addition, these suites make it possible to measure and compare Big Data systems and architectures and their ease of use. Finally, they are also used to evaluate applications, workloads, software system stacks and data sets. The goal is to determine the best practices of Big Data solution vendors.

How to make a Big Data Benchmark

This performance measurement can be more or less accurate depending on the needs of a company. Some players rely on manual labour. It consists of gathering information from a supplier’s documentation. This makes it possible to establish a profile of the tools and to know their theoretical performance. However, this information is not sufficient to choose a tool, software or platform. Therefore, companies opt for tools dedicated to this type of analysis. Companies also want to know more about the advantages of Machine Learning algorithms. MLPerf is one of the software packages designed for this purpose.

Big Data Benchmark: top of the best tool suites

Discover now a top of the best tool suites of Big Data Benchmarks.


The HiBench suite includes 10 typical micro workloads. It also provides options for users to enable input/output compression for most workloads with the zlib compression code.

AMP Benchmark

AMP Benchmark allows to measure the response time of different relational queries scans, aggregations, joins, and UDFs. It supports different data sizes. In particular, this suite is used for qualitative and quantitative comparison between five Big Data systems: Redshift, Hive, Shark, Impala, and Stinger/Tez.

These systems have very different sets of capabilities. Systems such as MapReduce (Shark/Hive) target flexible, large-scale calculations. They support UDFs, are error tolerant, and can be scalable at thousands of knots. Traditional MPP databases, on the other hand, are SQL compliant and optimized for relational queries. In fact, the workload is a set of queries that most of these systems can perform.


The CloudSuite benchmark suite is designed for emerging scale-out applications. Version 2.0 consists of eight applications selected according to their popularity in modern data centres.

The benchmarks are based on real-world software stacks. They also represent real-world configurations.


BigDataBench 3.1 includes 14 Real World Datasets and 33 Big Data workloads. In fact, it covers all types of data: structured, semi-structured and unstructured.

It also supports various data sources. These different types of sources include text, graphics, images, audio, video and data tables.


GridMix is a benchmark designed for Hadoop clusters. It submits a mixture of synthetic tasks, and models a profile from the production loads. Finally, this tool exists in three different versions. They are available under a creative commons license, and therefore totally free of charge.

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