Who are the big data analysts and why do they need analytical tools?
A big data analyst is the person who transforms data into information, insight, and business decision (Data à Information à Insight à Business decision). He is the person with the responsibility of collecting, organizing, and analyzing large sets of data or big data to detect patterns and some other useful information. A reliable data scientist should be able to perform data mining and data auditing efficiently.
Even if the data scientist possesses great data analyst skills, they still need the support of analytical tools to prepare their reports. There are special analytical tools available and made for data scientists. In this article, we will discuss top analytical tools for 2019 that a good data scientist should save in the diary to study.
1. Apache Spark
Apache Spark is a fast and common cluster computing system that offers high-level APIs in Scala, Python, Java, and R. It also provides an optimized engine that supports general execution graphs. Apache Spark also supports a rich set of professional tools like Spark SQL for SQL and structured data processing, MLib for machine learning, Spark Streaming, and GraphX for graph processing. There is a list of features that make Apache Spark one of the best tools for data analysts:
- Speed – Analysts can achieve high performance for both streaming and batch data using this tool.
- Ease of use – There are more than 80 professional operators that make it easy to build parallel apps.
- Generality – There is a stack of libraries that can combine within the same app.
- Flexibility – Apache Spark is flexible to work on everywhere. Analysts can run it on Apache Mesos, Hadoop, Kubernetes, etc.
2. Apache Storm
Apache Storm is an open-source distributed real-time computation system available for free. With Apache Storm, analysts are ready to process unbounded streams of data and perform real-time processing like Hadoop for batch processing. The features that make Apache Storm an ideal analytical tool are:
- Simple – Apache Storm is simple, and analysts can use it with any programming language.
- Fast – Analysts have the power to process over a million tuples per second per node using Apache Storm.
- Scalable – Apache Storm is scalable, fault-tolerant, and ensures data processing.
- Ease of use – Analysts can easily set up Apache Storm and operate.
3. Apache SAMOA
- Apache SAMOA is one of the platforms used by data scientists for mining big data streams. Using this analytical tool, data scientists can collect streaming algorithms distributed for data mining and machine learning tasks, including clustering, classification, and regression. Some features of Apache SAMOA that make it best tool for data scientists:
- Help Apache Spark Developers create ML algorithms easy to understand at the top of any distributed stream processing engine.
- Data scientists are ready to develop distributed streaming ML algorithms once and run them on numerous DSPEs.
4. Apache Hadoop
The Apache Hadoop software library is one of the frameworks used by data scientists to perform distributed processing of bulk data sets across clusters of computers with the help of simple programming models. The features that complete Apache Hadoop design are:
- Hadoop Common – The common utilities available in Apache Hadoop to support other Hadoop modules.
- Hadoop Distributed File System (HDFS) – A HDFS offers high throughput access to app data.
- HDFS YARN – It is a framework designed for job scheduling and cluster resource management.
- Hadoop MapReduce – It’s a YARN-based system intended for parallel processing of large sets of data.
- Hadoop Ozone – It is an object store for Hadoop.
- Hadoop Submarine – It is a machine learning engine made and designed for Hadoop.
5. Elasticsearch
Elasticsearch is an open-source full-text search and analytics engine that is intended for data scientists to let them store, search, and analyze bulk data faster and in near real-time. It is a distributed, RESTful search and analytics engine that has a feature of solving the increasing number of use cases. A few features are listed below-
- Query – With ElasticSearch, analysts are ready to perform and combine several types of searches – unstructured, structured, metric, geo- as per the requirement.
- Analyze – Aggregations of Elasticsearch let the data scientists zoom out to explore patterns and trends in their data.
- Speed – It is incredibly fast.
- Fast time to value – It offers simple REST-based APIs, HTTP interface, and uses JSON documents.
6. Rapid Miner
RapidMiner Studio is a robust and one of the powerful data mining tools available for data scientists. It is used for building predictive models. There are over a hundred data preparation and ML algorithms to support all data-mining projects. Using RapidMiner studio, analysts are ready to access, load, and analyze any kind of data – both structured and unstructured data.