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Benchmarking Big Data: Definition and Top 5 of the Best Tools

In order to know if a Big Data system will be efficient enough to meet the needs of the company, it is necessary to evaluate and compare it. This is called Benchmarking. To do this, we use suites of specialized tools called benchmarking Big Data. On the principle of the test bench, analysts test each of the hardware and software features offered by the suppliers.

These tool suites include micro benchmarks, component benchmarks and application benchmarks. First, 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.

Benchmark Big Data: What are the benefits?

The Benchmark Big Data suite has many advantages. They make it possible to analyze the memory hierarchy, to measure the intensity of operation, and to characterize the workloads.

In addition, these suites 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 datasets. This is to determine the best practices of sellers of Big Data solutions.

Benchmark Big Data: top of the best tool suites?

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

HiBench

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

AMP Benchmark

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

These systems have very different sets of capabilities. The systems like MapReduce (Shark / Hive) target flexible calculations and large scale. They support UDFs, tolerate errors, and can be scaled to thousands of nodes. For their part, traditional MPP databases are SQL compliant and optimized for relational queries. In fact, the workload is a set of queries that most of these systems can accomplish.

CloudSuite

The CloudSuite benchmark suite is designed for emerging scale-out applications. Version 2.0 consists of eight apps selected based on their popularity in modern data centers.

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

BigDataBench

BigDataBench is a collection of 14 real-world data sets and 33 Big Data workloads. In fact, it covers all types of data: structured, semi-structured and unstructured.

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

GridMix

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

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