10 Tools Data Engineers and Data Scientist Should Learn
In the information age, data centers collect large amounts of data. The data collected comes from various sources such as financial transactions, customer interactions, social media, and many other sources, and more importantly, accumulates faster.
Data can be diverse and sensitive and requires the right tools to make it meaningful as it has unlimited potential to modernize business statistics, information and change lives.
Big Data tools and Data scientists are prominent in such scenarios.
Such a large amount of diverse data makes it difficult to process using traditional tools and techniques such as Excel. Excel is not really a database and has a limit (65,536 rows) for storing data.
Data analysis in Excel shows poor data integrity. In the long run, data stored in Excel has limited security and compliance, very low disaster recovery rates, and no proper version control.
To process such large and diverse data sets, a unique set of tools, called data tools, are needed to examine, process, and extract valuable information. These tools let you dig deep into your data to find more meaningful insights and data patterns.
Dealing with such complex technology tools and data naturally requires a unique skill set, and that’s why data scientist plays a vital role in big data.
The importance of big data tools
Data is the building block of any organization and is used to extract valuable information, perform detailed analyses, create opportunities, and plan new businesses milestones and visions.
More and more data is created every day that must be stored efficiently and securely and recalled when needed. The size, variety, and rapid change of that data require new big data tools, different storage, and analysis methods.
According to a study, the global big data market is expected to grow to US $103 billion by 2027, more than double the market size expected in 2018.
Today’s industry challenges
The term “big data” has recently been used to refer to data sets that have grown so large that they are difficult to use with traditional database management systems (DBMS).
Data sizes are constantly increasing and today range from tens of terabytes (TB) to many petabytes (PB) in a single data set. The size of these data sets exceeds the ability of common software to process, manage, search, share, and visualize over time.
The formation of big data will lead to the following:
Quality management and improvement
Supply chain and efficiency management
Customer intelligence
Data analysis and decision making
Risk management and fraud detection
In this section, we look at the best big data tools and how data scientists use these technologies to filter, analyze, store and extract them when companies want the deeper analysis to improve and grow their business.
Apache Hadoop
Apache Hadoop is an open-source Java platform that stores and processes large amounts of data.
Hadoop works by mapping large data sets (from terabytes to petabytes), analyzing tasks between clusters, and breaking them into smaller chunks (64MB to 128MB), resulting in faster data processing.
To store and process data, data is sent to the Hadoop cluster, HDFS (Hadoop distributed file system) stores data, MapReduce processes data, and YARN (Yet another resource negotiator) divides tasks and assigns resources.
It is suitable for data scientists, developers, and analysts from various companies and organizations for research and production.
Features
Data replication: Multiple copies of the block are stored in different nodes and serve as fault tolerance in case of an error.
Highly Scalable: Offers vertical and horizontal scalability
Integration with other Apache models, Cloudera and Hortonworks
Consider taking this brilliant online course to learn Big Data with Apache Spark.
Rapidminer
The Rapidminer website claims that approximately 40,000 organizations worldwide use their software to increase sales, reduce costs and avoid risk.
The software has received several awards: Gartner Vision Awards 2021 for data science and machine learning platforms, multimodal predictive analytics, and machine learning solutions from Forrester and Crowd’s most user-friendly machine learning and data science platform in spring G2 report 2021.
It is an end-to-end platform for the scientific lifecycle and is seamlessly integrated and optimized for building ML (machine learning) models. It automatically documents every step of preparation, modeling, and validation for full transparency.
It is a paid software available in three versions: Prep Data, Create and Validate, and Deploy Model. It’s even available free of charge to educational institutions, and RapidMiner is used by more than 4,000 universities worldwide.
Features
It checks data to identify patterns and fix quality problems
It uses a codeless workflow designer with 1500+ algorithms
Integrating machine learning models into existing business applications
Tableau
Tableau provides the flexibility to visually analyze platforms, solve problems, and empower people and organizations. It is based on VizQL technology (visual language for database queries), which converts drag and drop into data queries through an intuitive user interface.
Tableau was acquired by Salesforce in 2019. It allows linking data from sources such as SQL databases, spreadsheets, or cloud applications like Google Analytics and Salesforce.
Users can purchase its versions Creator, Explorer, and Viewer based on business or individual preferences as each one has its own characteristics and functions.
It is ideal for analysts, data scientists, the education sector, and business users to implement and balance a data-driven culture and evaluate it through results.
Features
Dashboards provide a complete overview of data in the form of visual elements, objects, and text.
Large selection of data charts: histograms, Gantt charts, charts, motion charts, and many more
Row-level filter protection to keep data safe and stable
Its architecture offers predictable analysis and forecasting
Learning Tableau is easy.
Cloudera
Cloudera offers a secure platform for cloud and data centers for big data management. It uses data analytics and machine learning to turn complex data into clear, actionable insights.
Cloudera offers solutions and tools for private and hybrid clouds, data engineering, data flow, data storage, data science for data scientists, and more.
A unified platform and multifunctional analytics enhance the data-driven insight discovery process. Its data science provides connectivity to any system the organization uses, not only Cloudera and Hortonworks (both companies have partnered).
Data scientists manage their own activities such as analysis, planning, monitoring, and email notifications via interactive data science worksheets. By default, it is a security compliant platform that allows data scientists to access Hadoop data and run Spark queries easily.
The platform is suitable for data engineers, data scientists, and IT professionals in various industries such as hospitals, financial institutions, telecommunications, and many others.
Features
Supports all major private and public clouds, while the data Science workbench supports on-premises deployments
Automated data channels convert data into usable forms and integrate them with other sources.
Uniform workflow allows for fast model construction, training, and implementation.
Secure environment for Hadoop authentication, authorization, and encryption
Conclusion
The big data list above includes the paid tools and open source tools. Brief information and functions are provided for each tool. If you are looking for descriptive information, you can visit the relevant websites.
The companies looking to gain a competitive advantage use big data and related tools like AI (artificial intelligence), ML (machine learning), and other technologies to take tactical actions to improve customer service, research, marketing, future planning, etc.
Big data tools are used in most industries since small changes in productivity can translate into significant savings and big profits. We hope the article above gave you an overview of big data tools and their significance.