Top Big Data Tools & Software for 2023

Top Big Data tools

Hadoop: Best for large-scale data processing

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Pros

• Open-source structure is cost-effective
• Replication feature ensures data is copied to multiple nodes
• Handles structured and unstructured data

Cons

• Inefficient for handling small datasets
• Struggles with real-time data processing

Key features

Hadoop Distributed File System (HDFS): HDFS is a distributed file system designed to store and provide high-throughput access to large amounts of data, distributing it across nodes in a cluster and replicating blocks for fault tolerance.

MapReduce: This is Hadoop’s data processing model that allows for parallel processing of large data sets by dividing the tasks into smaller parts and then combining the results.

YARN (Yet Another Resource Negotiator): YARN is Hadoop’s cluster management technology responsible for managing resources and scheduling tasks.Hadoop Common: Hadoop Common provides the essential libraries and utilities needed by other Hadoop modules, forming the underlying infrastructure to support other Hadoop components.

Pricing

Data Engineering

• Core: $0.07 per CCU/hour
• All-Purpose: $0.20 per CCU/hour*

Data Warehouse

• $0.07 per CCU/hour

Operational Database

• $0.08 per CCU/hour

Machine Learning

• $0.20 per CCU/hour**

Data Hub

• $0.04 per CCU/hour**

Flow Management on Data Hub

• $0.15 per CCU/hour

DataFlow

• Deployments & Test Sessions: $0.30 per CCU/hour
• Functions: Starting at $0.10 per Billable Invocation (with volume discounts)
• *The All-Purpose pricing under Data Engineering is currently a tech preview and will take effect upon GA.
• *GPU Instances per CCU prices may vary under Machine Learning and Data Hub.

Why we chose Hadoop

We recognized Hadoop due to its unparalleled scalability, robust fault tolerance, and cost-effectiveness. Its cornerstone components, HDFS and MapReduce, excel in handling immense data volumes across numerous nodes, providing high-throughput access and parallel processing. Furthermore, Hadoop’s open-source nature and ability to run on commodity hardware make it a pocket-friendly solution for businesses. So, for handling large-scale data processing efficiently and economically, Hadoop is our standout choice.

Hadoop is an open-source software framework developed by Apache for storing and processing large volumes of data across clusters of computers. It employs a distributed file system, HDFS, that splits files into large blocks and distributes them across nodes in a cluster, ensuring efficient data processing. The core of Hadoop, MapReduce, is a programming model that enables the processing of large data sets. By leveraging Hadoop, organizations can handle vast amounts of data efficiently, making it a mainstay in the realm of Big Data.

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Apache Spark: Best for real-time analytics

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Pros

• Fast data processing capabilities
• Extensive high-level APIs and built-in modules
• Inbuilt fault tolerance

Cons

• Substantial memory consumption
• Limited support for Windows functions
• Lack of real-time processing

Key features

In-memory computing: Apache Spark utilizes in-memory computing capabilities to enhance the speed of data processing tasks, thus accelerating analytical applications.

Advanced analytics: Spark supports advanced analytics functionalities such as GraphX for graph processing and MLib for machine learning, enabling sophisticated data analysis.

Real-time stream processing: With Spark Streaming, you can ingest real-time data in mini-batches and perform transformations or actions on the streams, enabling live data analytics.

Databricks runtime: This proprietary, cloud-optimized runtime environment enhances Spark’s performance, security, and reliability, delivering optimized execution in modern cloud hardware architectures.

Pricing

Free version available

Contact Apache for customized pricing information

Why we chose Apache Spark

We selected Apache Spark as the top tool for real-time analytics because it excels in swift data processing, enabled by its in-memory computing feature. This capability allows Spark to perform analytics much faster, essential for real-time insights. Another critical factor is Spark Streaming, which supports live data ingestion and transformation, enabling instant decision-making. Moreover, Spark’s built-in modules for advanced analytics, such as GraphX for graph processing and MLib for machine learning, allow for sophisticated, real-time data analyses, setting Spark apart in the field.

Apache Spark is an open-source, distributed computing system used for big data processing and analytics. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Spark can handle both batch and real-time analytics, distinguishing it from traditional Hadoop MapReduce paradigm.

The Spark Core is complemented by a set of powerful, higher-level libraries which can be seamlessly used in the same application. These libraries include SparkSQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

Designed to be highly accessible, Spark supports programming in Java, Python, R, and Scala, and includes over 100 operators for transforming data and familiar data frame APIs for manipulating semi-structured data.

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Google BigQuery: Best for data handling in Google Cloud

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Pros

• Serverless architecture
• Integrated machine learning and AI
• Geospatial analysis
• Real-time analytics

Cons

• Challenging to predict and manage costs
• Regular updates, deletes, and row manipulations can result in additional storage costs
• Lack of user control over infrequently accessed data

Key features

Serverless Architecture: BigQuery offers a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility, eliminating the need to manage infrastructure.

BigQuery ML: This unique feature enables data scientists and data analysts to build and operationalize machine learning models on planet-scale structured or semi-structured data, directly inside BigQuery using SQL.

BigQuery GIS: Integrates with Google Maps Platform to provide geospatial analytics capabilities, allowing users to analyze and visualize geospatial data using standard SQL geography functions.

BigQuery BI Engine: An in-memory analysis service with sub-second query response time and high concurrency for BI workloads, BI Engine seamlessly integrates with familiar tools like Google Data Studio to accelerate data exploration and analysis.

Pricing

Consult Google’s comprehensive pricing sheet for your specific business requirements

Why we chose Google BigQuery

We selected Google thanks to its impressive features and seamless integration. The platform’s serverless architecture eliminates the need for infrastructure management, enabling you to focus more on deriving insights from your data. Its unique capabilities, like BigQuery ML and BigQuery GIS, allow complex machine learning and geospatial analyses using simple SQL queries. BigQuery’s BI Engine further enhances data exploration and analysis with its swift query response time. The ease of integration with Google Cloud services and its superior scalability make BigQuery an excellent choice for those using Google Cloud.

Google BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. It allows you to analyze large datasets by running SQL-like queries in a highly scalable and cost-effective manner. BigQuery is unique in its provision for machine learning capabilities with BigQuery ML, geospatial analysis with BigQuery GIS, and advanced business intelligence with its BI Engine. This tool can ingest and process real-time data, making it ideal for businesses that require immediate insights. Its integration with Google Cloud services makes it a popular choice for handling data in the Google Cloud ecosystem.

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Snowflake: Best for cloud-based data warehousing

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Pros

• Excels in data sharing capabilities
• Separates compute and storage
• Simplicity of user due to intuitive interface and SQL-based queries
• Highly scalable

Cons

• Limited ETL capabilities
• Limited support for unstructured data
• Performance degradation with complex queries that involve large joins

Key features

Multi-cluster shared data architecture: Snowflake’s unique architecture separates storage and compute, enabling users to scale each independently, thus improving performance and cost-efficiency.

Zero-copy cloning: This feature allows for the rapid duplication of databases, schemas, or tables without impacting performance or storage costs, facilitating testing, development, and data recovery.

Time Travel: Snowflake’s Time Travel feature lets you access historical data at any point within a defined period, providing the ability to restore deleted objects or compare data changes over time.

Data sharing: Snowflake’s Secure Data Sharing feature allows users to share live, ready-to-query data across different Snowflake accounts without data movement or copies, enhancing collaboration.

Pricing

Storage: Costs are measured based on the average amount of data stored in Snowflake monthly, after compression. This cost is approximately $23 per 1TB of data per month.

Virtual Warehouse (Compute): The cost depends on the size of the warehouse, the number of clusters, and the time spent executing queries. Costs are paid via Snowflake credits, which are not spent when the warehouse is not running or in suspended mode. Here’s the credit consumption rate for different warehouse sizes:

X-Small (1 server): 1 credit/hour

Small (2 servers): 2 credits/hour

Medium (4 servers): 4 credits/hour

Large (8 servers): 8 credits/hour

X-Large (16 servers): 16 credits/hour

2X-Large (32 servers): 32 credits/hour

3X-Large (64 servers): 64 credits/hour

4X-Large (128 servers): 128 credits/hour

Cloud Services: Cloud services manage end-to-end tasks. Snowflake offers free usage of cloud services up to 10% of daily compute credits.

Serverless Features: Additional serverless features like Snowpipe, Database Replication, Materialized Views Maintenance, Automatic Clustering, Search Optimization Service consume Snowflake credits when used.

*Snowflake only charges when resources are actively being used.

Why we chose Snowflake

We consider Snowflake as the best option for cloud-based data warehousing due to its unique combination of flexibility, performance, and user-friendliness. Snowflake’s distinctive architecture allows for instant scalability, providing efficient performance even with large datasets. Its interface is user-friendly and its data sharing capabilities are superior, enhancing collaboration across organizations. The separation of storage and compute resources ensures cost efficiency, while zero-copy cloning and the time travel feature enhance data management and recovery. When coupled with Snowflake’s commitment to security and its excellent support for structured and semi-structured data, it becomes a compelling choice for businesses looking for a cloud-based data warehouse solution.

Snowflake is a cloud-based data warehousing platform that leverages a unique architecture to allow for efficient data storage and analytics. It separates storage and compute resources, enabling users to scale each independently for cost efficiency and improved performance. Snowflake supports structured and semi-structured data formats and uses SQL-based queries, making it easy for those familiar with SQL to use. Key features include zero-copy cloning for rapid data duplication, Time Travel for accessing historical data, and Secure Data Sharing for live data sharing across Snowflake accounts. With Snowflake, businesses can securely store, integrate, and analyze their data.

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Tableau: Best for data visualization

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Pros

• Excellent data visualization features
• User-friendly design
• Seamless connectivity and integration
• Advanced analytics

Cons

• Limited transformation and ETL capabilities
• Limited data preparation features
• Resource intensive

Key features

Drag-and-drop analytics: Tableau’s drag-and-drop interface enables users to create interactive visualizations by simply dragging and dropping data elements onto the canvas, making it easy to explore and analyze data intuitively.

Data blending and real-time data connection: Tableau allows users to blend data from multiple sources and establish real-time connections to live data, ensuring up-to-date insights and analysis.

Smart recommendations and insights: Tableau employs AI-driven smart features to provide automatic recommendations and insights, helping users uncover hidden patterns and trends in their data effortlessly.

Data storytelling and collaboration: Tableau enables users to create interactive data stories that combine visualizations, text, and media to effectively communicate data insights. It also offers collaboration features, allowing teams to share and collaborate on dashboards and visualizations, facilitating data-driven decision-making.

Pricing

Free trial available

Free version available

Tableau Viewer: $15/user/month

Tableau Explorer: $40/user/month

Tableau Creator: $70/user/month

Why we chose Tableau

Tableau stands out as our top choice for data visualization due to its unrivaled combination of powerful features and user-friendly design. Its intuitive drag-and-drop interface allows users of all skill levels to create captivating visualizations effortlessly. With extensive data connectivity options, Tableau seamlessly integrates with various data sources, enabling comprehensive analysis. The platform’s advanced analytics capabilities and AI-driven recommendations provide valuable insights, while its collaborative features foster teamwork and knowledge sharing. From interactive dashboards to compelling data storytelling, Tableau empowers users to effectively communicate data insights, making it an indispensable tool for data visualization.

Tableau is a leading data visualization and business intelligence tool that enables users to analyze and present data in visually compelling ways. With its intuitive interface and drag-and-drop functionality, Tableau simplifies the process of creating interactive dashboards, charts, and graphs. It offers extensive data connectivity options, allowing users to connect to various data sources for real-time analysis. Tableau’s advanced analytics capabilities and AI-driven insights empower users to uncover hidden patterns and make data-driven decisions. Its collaborative features facilitate sharing and collaboration among teams, making Tableau a go-to solution for effective data visualization and analysis.

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PowerBI: Best for in-depth analysis

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Pros

• Large and active community
• Excellent data security
• Seamless integration with Microsoft ecosystem
• Collaboration and sharing features

Cons

• Complex data modeling
• Limited native data transformation options
• Dependency on Microsoft stack

Key features

Interactive reports and dashboards: Power BI enables users to create interactive and dynamic reports and dashboards that allow for seamless exploration and analysis of data, empowering users to gain insights and make data-driven decisions.

Natural language query (Q&A): Power BI’s Natural Language Query (Q&A) feature allows users to ask questions about their data using everyday language and receive instant visualizations and insights, making data analysis more intuitive and accessible to a wider audience.

Data connectivity and integration: Power BI offers extensive connectivity to a wide range of data sources, both on-premises and in the cloud, allowing users to bring together disparate data sources and create comprehensive reports and visualizations.

AI-driven insights with Quick Insights: Power BI incorporates AI capabilities through Quick Insights, which automatically analyzes data to discover hidden patterns, trends, and outliers, providing users with valuable and actionable insights without requiring manual exploration or analysis.

Pricing

Free trial available

Power BI Pro: $9.99/user/month 

Power BI Premium: $20/user/month OR $4,995/capacity/month

Why we chose Power BI

We chose Power BI as a preferred data visualization and business intelligence tool for several reasons. First, its intuitive user interface and drag-and-drop functionality make it accessible to users of all skill levels, enabling them to create compelling visualizations with ease. Additionally, Power BI seamlessly integrates with the Microsoft ecosystem, allowing for smooth data connectivity and collaboration with other Microsoft tools. The extensive range of data connectors and the ability to handle real-time streaming data further enhance its capabilities. Moreover, Power BI’s AI-driven features, such as natural language query and Quick Insights, provide valuable insights and make data analysis more intuitive. Overall, Power BI offers a comprehensive solution with a strong balance of user-friendly features, integration capabilities, and advanced analytics functionalities, making it an ideal choice for data visualization and analysis needs.

Power BI is a powerful business intelligence and data visualization tool offered by Microsoft. It enables users to connect to various data sources, transform raw data into meaningful insights, and create interactive visualizations, reports, and dashboards. Power BI provides a user-friendly interface with drag-and-drop functionality, making it accessible to users of all skill levels. It supports real-time data streaming, collaboration, and sharing features, allowing teams to collaborate on data analysis and decision-making. With its extensive integration capabilities, Power BI seamlessly integrates with other Microsoft products and services, providing a comprehensive solution for data analytics and business intelligence needs.

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Databricks: Best for team collaboration

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Pros

• Unified analytics platform
• Leverages distributed computing and cloud resources to handle large-scale data processing
• Extensive collaborative features
• Advanced analytics and machine learning

Cons

• Complexity of use
• Cost
• Limited native data visualization

Key features

Unified Analytics Platform: Databricks offers a unified platform that combines data engineering, data science, and business analytics, providing a collaborative and streamlined environment for end-to-end data processing and analysis.

Apache Spark Integration: Databricks leverages the power of Apache Spark, a fast and scalable analytics engine, enabling high-performance processing and analytics on big data sets.

Collaborative Workspace: Databricks provides a collaborative workspace where teams can work together on projects, share code, notebooks, and visualizations, facilitating seamless collaboration and knowledge sharing.

Delta Lake: Databricks includes Delta Lake, an open-source storage layer that provides ACID transactions, schema enforcement, and data versioning capabilities, ensuring data reliability, consistency, and simplifying data pipeline management.

Pricing

Free trial available

Workflows & Streaming: Jobs

• Starting at $0.07/Databricks Unit (DBU)
• Run data engineering pipelines to build data lakes and manage data at scale.

Workflows & Streaming: Delta Live Tables

• Starting at $0.20/DBU
• Build high-quality streaming or batch ETL pipelines using Python or SQL with the DLT edition that is best for your workload.

Data Warehousing: Databricks SQL

• Starting at $0.22/DBU
• Run SQL queries for BI reporting, analytics, and visualization to get timely insights from data lakes. Available in both Classic and Serverless (managed) Compute.

Data Science & Machine Learning: All Purpose Compute for Interactive Workloads

• Price: Starting at $0.40/DBU
• Run interactive data science and machine learning workloads. Also suitable for data engineering, BI, and data analytics.

Data Science & Machine Learning: Serverless Real-Time Inference

• Starting at $0.07 per DBU
• Make live predictions in your apps and websites.

Why we chose Databricks

With Databricks’ collaborative workspace, multiple team members can work together on projects, share code, notebooks, and visualizations in real-time. This fosters seamless collaboration, allowing teams to collectively analyze data, build models, and make data-driven decisions. The platform also enables version control, making it easier to track changes and collaborate on shared projects. Databricks promotes a collaborative environment that empowers teams to effectively collaborate, leverage each other’s expertise, and collectively drive insights and innovation.

Databricks is a unified analytics platform designed to simplify and accelerate the process of building data-driven applications. It combines data engineering, data science, and business analytics in one collaborative workspace. Databricks leverages the power of Apache Spark, an open-source analytics engine, to provide scalable and high-performance processing and analysis of large datasets. It offers a user-friendly interface, interactive notebooks, and collaborative features, enabling teams to work together on projects, share code, and gain valuable insights from data. Databricks supports a wide range of data sources, provides advanced analytics capabilities, and integrates with popular machine learning frameworks, making it a comprehensive platform for data engineering, data science, and business intelligence tasks.

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Key categories of big data software

Data storage and management

Big data tools in this category focus on efficiently storing and managing large volumes of data. They include distributed file systems like Apache Hadoop Distributed File System (HDFS), cloud-based storage solutions such as Amazon S3, and NoSQL databases like MongoDB. These tools provide scalable and robust solutions for organizing, storing, and accessing structured and unstructured data.

ALSO READ: 5 Best Data Storage Solutions for BI

Data Processing

Data processing tools are designed to handle the complexities of processing and manipulating big data. They enable distributed computing and parallel processing, allowing for faster and efficient data processing. Apache Hadoop, Apache Spark, and stream processing frameworks like Apache Flink and Apache Kafka are prominent tools in this category.

Data analysis

Big data analysis tools focus on extracting insights and patterns from large datasets. They provide capabilities for querying, aggregating, and analyzing data to uncover meaningful information. Tools like Apache Hive and Apache Pig offer SQL-like query languages, while machine learning frameworks like TensorFlow and Scikit-learn enable advanced analytics and predictive modeling.

Data visualization

Data visualization tools aid in presenting complex data in a visual and interactive format. They help users understand and communicate data insights through charts, graphs, and dashboards. Popular tools in this category include Tableau, Power BI, and open-source libraries like D3.js. These tools facilitate exploratory data analysis, making it easier to identify patterns and trends.

The importance of big data tools for businesses

Big data tools serve as a cornerstone for businesses, propelling innovation, fostering sustainable growth, and driving digital transformation.

By leveraging big data analytics, organizations can draw valuable insights from massive data sets, enabling them to predict customer behavior, identify market trends, and improve overall performance. These tools also facilitate process automation, helping businesses reduce operational costs and time-consuming manual tasks, thus enhancing productivity. Further, the strategic application of big data can also aid in risk management, helping enterprises anticipate and mitigate potential threats. Equally crucial is the role of big data tools in creating personalized customer experiences, which is pivotal in driving customer loyalty and business growth.

The future of big data software

As we look ahead, the future of big data tools is nothing short of revolutionary. Augmented analytics, powered by advanced AI and machine learning, is expected to redefine the landscape, automating data preparation and interpretation processes. This will democratize data analytics, making it accessible to non-technical users, thus driving data-driven decision making across all business levels. Additionally, real-time analytics will play a more prominent role, enabling businesses to respond to trends and anomalies instantaneously. The integration of privacy-enhancing technologies with big data tools is another trend to watch, given the increasing focus on data security and regulatory compliance. Overall, the evolution of big data tools will continue to unlock new frontiers, fostering unprecedented business growth and innovation.

ALSO READ: Top Prescriptive Analytics Tools

Finding the right big data tools for your business (Conclusion)

 The quest to find the right big data tools for your business is a critical journey that can significantly influence your operational effectiveness and competitive standing. It’s essential to align this choice with your specific business objectives, technical infrastructure, and team expertise. As we navigate the dynamic digital terrain, the ability to adapt and adopt scalable solutions is key. Future-proof your business by harnessing advanced technologies like AI and machine learning, but without overlooking the importance of data privacy and security. Embrace the power of big data and ride the wave of this digital revolution towards unparalleled business growth and innovation.

For a comprehensive list providing the top BI software tools on the market today, check out our Business Intelligence Software Buyer’s Guide. 

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Methodology for selection and evaluation

Market research: We began with extensive market research to identify widely used big data tools in various industries.

User reviews and expert opinions: We studied user reviews from trusted platforms and sought expert opinions to understand the strengths and weaknesses of each tool.

Tool capabilities: We evaluated the tools based on their capabilities, such as data collection, storage, processing, and visualization, as well as their integration capabilities with other platforms.

Scalability: We considered how scalable each tool was, meaning how well it could handle increasing data volume.

Security: We evaluated the data security features of each tool, considering the increasing importance of data privacy.

Ease of use: We also took into account the tool’s user-friendliness, which impacts the speed of implementation and adoption rate within an organization.

Cost effectiveness: Lastly, we factored in the cost of each tool, comparing the features offered relative to their price.

The culmination of this thorough, multi-faceted examination resulted in a comprehensive, balanced assessment, enabling us to present you with the top big data tools for your business needs.

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