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
Apache Hive
Apache Hive is an open-source project developed on top of Apache Hadoop. It allows reading, writing, and managing large datasets available in various repositories and allows users to combine their own functions for custom analysis.
Hive is designed for traditional storage tasks and is not intended for online processing tasks. Its robust batch frames offer scalability, performance, scalability, and fault tolerance. It is suitable for data extraction, predictive modeling, and indexing documents. Not recommended for querying real-time data as it introduces latency in getting results. Features
Supports MapReduce, Tez, and Spark computing engine Process huge data sets, several petabytes in size Very easy to code compared to Java Provides fault tolerance by storing data in the Apache Hadoop distributed file system
Apache Storm
The Storm is a free, open-source platform used to process unlimited data streams. It provides the smallest set of processing units used to develop applications that can process very large amounts of data in real-time.
A storm is fast enough to process one million tuples per second per node, and it is easy to operate. Apache Storm allows you to add more nodes to your cluster and increase application processing power. Processing capacity can be doubled by adding nodes as horizontal scalability is maintained. Data scientists can use Storm for DRPC (Distributed Remote Procedure Calls), real-time ETL (Retrieval-Conversion-Load) analysis, continuous computation, online machine learning, etc. It is set up to meet the real-time processing needs of Twitter, Yahoo, and Flipboard. Features
Easy to use with any programming language It is integrated into every queuing system and every database. Storm uses Zookeeper to manage clusters and scales to larger cluster sizes Guaranteed data protection replaces lost tuples if something goes wrong
Snowflake Data Science
The biggest challenge for data scientists is preparing data from different resources, as maximum time is spent retrieving, consolidating, cleaning, and preparing data. It is addressed by Snowflake. It offers a single high-performance platform that eliminates the hassle and delay caused by ETL (Load Transformation and Extraction). It can also be integrated with the latest machine learning (ML) tools and libraries such as Dask and Saturn Cloud.
Snowflake offers a unique architecture of dedicated compute clusters for each workload to perform such high-level computing activities, so there is no resource sharing between data science and BI (business intelligence) workloads. It supports data types from structured, semi-structured (JSON, Avro, ORC, Parquet, or XML) and unstructured data. It uses a data lake strategy to improve data access, performance, and security. Data scientists and analysts use snowflakes in various industries, including finance, media and entertainment, retail, health and life sciences, technology, and the public sector. Features
High data compression to reduce storage costs Provides data encryption at rest and in transit Fast processing engine with low operational complexity Integrated data profiling with table, chart, and histogram views
Datarobot
Datarobot is a world leader in the cloud with AI (Artificial Intelligence). Its unique platform is designed to serve all industries, including users and different types of data. The company claims the software is used by a third of Fortune 50 companies and provides more than a trillion estimates across various industries.
Dataroabot uses automated machine learning (ML) and is designed for enterprise data professionals to quickly create, adapt and deploy accurate forecast models. It gives scientists easy access to many of the latest machine learning algorithms with complete transparency to automate data preprocessing. The software has developed dedicated R and Python clients for scientists to solve complex data science problems. It helps automate data quality, feature engineering, and implementation processes to ease data scientist activities. It is a premium product, and the price is available on request. Features
Increases the business value in terms of profitability, forecasting simplified Implementation processes and automation Supports algorithms from Python, Spark, TensorFlow, and other sources. API integration lets you choose from hundreds of models
TensorFlow
TensorFlow is a community AI (artificial intelligence) based library that uses data flow diagrams to build, train and deploy machine learning(ML) applications. This allows developers to create large layered neural networks.
It includes three models – TensorFlow.js, TensorFlow Lite, and TensorFlow Extended (TFX). Its javascript mode is used for training and deploying models in the browser and on Node.js at the same time. Its lite mode is for deploying models on mobile and embedded devices, and the TFX model is for preparing data, validating, and deploying models. Due to its robust platform, it could be deployed on servers, edge devices, or the web regardless of programming language. TFX contains mechanisms to enforce ML pipelines that can be ascendable and provide robust overall performance duties. The Data engineering pipelines like Kubeflow and Apache Airflow support TFX. The Tensorflow platform is suitable for Beginner. Intermediate and for experts to train a generative adversarial network to generate images of handwritten digits using Keras. Features
Can deploy ML models on-premise, cloud, and in the browser and regardless of language Easy model building using innate APIs for speedy model repetition Its various add-on libraries and models support research activities to experiment Easy model building using multiple levels of abstraction
Matplotlib
Matplotlib is a comprehensive community software for visualizing animated data and graphic graphics for the Python programming language. Its unique design is structured so that a visual data graph is generated using a few lines of code. There are various third-party applications such as drawing programs, GUIs, color maps, animations, and many more that are designed to be integrated with Matplotlib. Its functionality can be extended with many tools such as Basemap, Cartopy, GTK-Tools, Natgrid, Seaborn, and others. Its best features include drawing graphs and maps with structured and unstructured data.
Bigml
Bigml is a collective and transparent platform for Engineers, data scientists, developers, and analysts. It performs end-to-end data transformation into actionable models. It effectively creates, experiments, automates, and manages ml workflows, contributing to intelligent applications across a wide range of industries.
This programmable ML (machine learning) platform helps with sequencing, time series prediction, association detection, regression, cluster analysis, and more. Its fully manageable version with single and multiple tenants and one possible deployment for any cloud provider makes it easy for enterprises to give everyone access to big data. Its price starts at $30 and is free for small datasets and educational purposes, and is used in over 600 universities. Due to its robust engineered ML algorithms, it is suitable in various industries such as pharmaceutical, entertainment, automotive, aerospace, healthcare, IoT, and many more. Features
Automate time-consuming and complex workflows in a single API call. It can process large amounts of data and perform parallel tasks The library is supported by popular programming languages such as Python, Node.js, Ruby, Java, Swift, etc. Its granular details ease the job of auditing and regulatory requirements
Apache Spark
It is one of the largest open-source engines widely used by large companies. Spark is used by 80% of Fortune 500 companies, according to the website. It is compatible with single nodes and clusters for big data and ML.
It is based on advanced SQL (Structured Query Language) to support large amounts of data and work with structured tables and unstructured data. The Spark platform is known for its ease of use, large community, and lightning speed. The Developers use Spark to build applications and run queries in Java, Scala, Python, R, and SQL. Features
Processes data in batch as well as in real-time Supports large amounts of petabytes of data without downsampling It makes it easy to combine multiple libraries like SQL, MLib, Graphx, and Stream into a single workflow. Works on Hadoop YARN, Apache Mesos, Kubernetes, and even in the cloud and has access to multiple data sources
Knime
Konstanz Information Miner is an intuitive open-source platform for data science applications. A data scientist and analyst can create visual workflows without coding with simple drag-and-drop functionality.
The server version is a trading platform used for automation, data science management, and management analysis. KNIME makes data science workflows and reusable components accessible to everyone. Features
What is the importance of the 5 V’s of big data?
The 5 V’s of big data helps data scientists understand and analyze big data to gain more insights. It also helps provide more statistics useful for businesses to make informed decisions and gain a competitive advantage. Volume: Big data is based on volume. Quantum volume determines how big the data is. Usually contains a large amount of data in terabytes, petabytes, etc. Based on volume size, data scientists plan various tools and integrations for data set analysis. Velocity: The speed of data collection is critical because some companies require real-time data information, and others prefer to process data in packets. The faster the data flow, the more data scientists can evaluate and provide relevant information to the company. Variety: Data comes from different sources and, importantly, not in a fixed format. Data is available in structured (database format), semi-structured (XML/RDF) and unstructured (binary data) formats. Based on data structures, big data tools are used to create, organize, filter, and process data. Veracity: The Data accuracy and credible sources define the big data context. The data set comes from various sources such as computers, network devices, mobile devices, social media, etc. Accordingly, the data must be analyzed to be sent to its destination. Value: Finally, how much is a company’s big data worth? The role of the data scientist is to make the best use of data to demonstrate how data insights can add value to a business.
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. You may also like: Online courses to learn the basics of Data Engineering.
