Data Science Tools For Data Storage
Apache Hadoop
Apache Hadoop is a free, open-source framework that can manage and store tons and tons of data. It provides distributed computing of massive data sets over a cluster of 1000s of computers. It is used for high-level computations and data processing.
Here’s a list of features of Apache Hadoop:
- Effectively scale large data on thousands of Hadoop clusters
- It uses the Hadoop Distributed File System (HDFS) for data storage which distributes massive amounts of data across several nodes for distributed, parallel computing
- Provides the functionality of other data processing modules, such as Hadoop MapReduce, Hadoop YARN, and so on
Microsoft HD Insights
Azure HDInsight is a cloud platform provided by Microsoft for the purpose of data storage, processing, and analytics. Enterprises such as Adobe, Jet, and Milliman use Azure HD Insights to process and manage massive amounts of data.
Here’s a list of features of Microsoft HD Insights:
- It provides full-fledged support to integrate with Apache Hadoop and Spark clusters for data processing
- Windows Azure Blob is the default storage system for Microsoft HD Insights. It can effectively manage the most sensitive data across thousands of nodes
- Provides Microsoft R Server that supports enterprise-scale R for performing statistical analysis and building strong Machine Learning models.
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Data Science Tools for Exploratory Data Analysis
Informatica PowerCenter
The buzz around Informatica is explained by the fact that their revenue has rounded off to around $1.05 billion. Informatica has many products focused on data integration. However, Informatica PowerCenter stands out due its data integration capabilities.
Here’s a list of features of Informatica PowerCenter:
- A data integration tool that is based on the ETL (Extract Transform Load) architecture.
- It aids in extracting data from various source, transforming and processing it in accordance with business requirements and finally loading or deploying it into a warehouse.
- It provides support for distributed processing, grid computing, adaptive load balancing, dynamic partitioning, and pushdown optimization.
RapidMiner
There is no surprise that RapidMiner is one of the most popular tools for implementing Data Science. RapidMiner ranked at number 1 in the Gartner Magic Quadrant for Data Science Platforms 2017, and in the Forrester Wave for predictive analytics and Machine Learning, and one of the top performers in the G2 Crowd predictive analytics grid.
Here’s are some of its features:
- A single platform for data processing, building Machine Learning models and deployment.
- It provides support for integrating Hadoop framework with its in-built RapidMiner Radoop
- Models Machine Learning algorithms by using a visual workflow designer. It can also generate predictive models through automated modeling
Data Science Tools for Data Modelling
H2O.ai
H2O.ai is the company behind open-source Machine Learning (ML) products like H2O, aimed to make ML easier for all.
With around 130,000 data scientists and approximately 14,000 organizations, the H20.ai community is growing at a strong pace. H20.ai is an open-source Data Science tool that is aimed at making data modeling easier.
Here are some of its features:
- It was built using the most popular Data Science programming languages, i.e., Python and R. This makes it easier to apply Machine Learning since most of the developers and data scientist are familiar with R and Python.
- It can implement a majority of the Machine Learning algorithms including generalized linear models (GLM), Classification Algorithms, Boosting Machine Learning and so on. It also provides support for Deep Learning.
- It provides support to integrate with Apache Hadoop to process and analyze huge amounts of data.
DataRobot
DataRobot is an AI-driven automation platform, that aids in developing accurate predictive models. DataRobot makes it easy to implement a wide range of Machine Learning algorithms, including clustering, classification, regression models.
Here are some of its features:
- Supports parallel programming by allowing the use of thousands of servers to perform simultaneous data analysis, data modeling, validation and so on.
- It builds, tests and trains Machine Learning models at a lightning-fast speed. DataRobot tests the models on several use cases and compares to see which model gives makes the most accurate predictions.
- Implements the whole Machine Learning process at a large scale. It makes model evaluation easier and effective by implementing parameter tuning and many other validation techniques.
Data Science Tools for Data Visualization
Tableau
Tableau is the most popular data visualization tool used in the market. It allows you to break down raw, unformatted data into a processable and understandable format. Visualizations created by using Tableau can easily help you understand the dependencies between the predictor variables.
Here are a few features of Tableau:
- It can be used to connect to multiple data sources, and it can visualize massive data sets to find correlations and patterns.
- The Tableau Desktop feature allows you to create customized reports and dashboards to get real-time updates
- Tableau also provides cross-database join functionality that allows you to create calculated fields and join tables, this helps in solving complex data-driven problems.
QlikView
QlikView is another data visualization tool that is used by more than 24,000 organizations worldwide. It is one of the most effective visualization platforms for visually analyzing data to derive useful business insights.
Here are a few features of QlikView:
- It provides clear visualizations to create dashboards and detailed reports that deliver a precise understanding of the data.
- It provides in-memory data processing, which creates reports and communicates it very fast to the end-users.
- Data association is another important feature of QlikView. It has patented in-memory technology that automatically generates associations and relations in data.
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Now that you know the top Data Science Tools, I’m sure you’re curious to learn more. Here are a few blogs that will help you get started with Data Science:
- A Complete Guide To Math And Statistics For Data Science
- 5 Data Science Projects – Data Science Projects For Practice
- Who is a Data Scientist?
- Top 10 Data Science Applications
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