The Best Big Data Tools in 2023

Best big data tool for data processing: Keboola

Data processing tools help you integrate vast amounts of raw data from the data sources to your destination. Additionally, these big data technologies help you clean and sanitize the data either in transit (ETL design) or after the data has been loaded (ELT design). The challenge with big data integration is the volume, velocity, and variability at which the data is produced at the source. 

Keboola is the best data platform that can build big data pipelines across your data integration and data processing use cases. You don't have to just take our word for it, our 4.7 rating on G2 speaks for itself.

Key features of Keboola

• Accelerated data pipeline development with automation. Set up data pipelines in minutes with over 250 pre-built components.
• Easy to use. Help your technical data experts to speed up their operations with low-code features, including Python, SQL, R, Julia, dbt, and CLI. Or allow your domain experts to build big data models and complex data use cases without writing a single line of code, using Keboola’s no-code features, such as visual builder and canned transformations. 
• Automated DevOps. Keboola takes care of the back office work, so you can enjoy a high-performance data platform without the maintenance hiccups. From dynamic backends to CDC replications and self-healing data pipelines, Keboola is a fault-tolerant and scalable big data technology you can rely on.
• Supports both on-premise and cloud-based data processing.
• Data management out-of-the-box. Keboola offers many features that allow you to manage big data without coding them yourself. Some of these include version control, development branches, sandboxes, and data lineage.

Runner-ups for best big data tool for data integration and data processing

• Apache Storm. An open-source computation framework that is ideal for stream processing. Apache Storm can integrate real-time data streams via distributed processing. It’s similar to MapReduce’s batch processing, so it’ss powerful, but not the easiest to use. 
• Informatica PowerCenter. An ETL platform for large enterprises (think Fortune 1000). Informatica PowerCenter is a market leader for high-performance and large-scale data integration. Check Informatica PowerCenter’s strengths and weaknesses.

Best big data tool for storage: Snowflake

Big data storage is not just about the large data sets. It’s also about what is stored.

Big data is different from traditional data sets because it does not come just as structured or semi-structured data (JavaScript Object Notation or JSON documents, XML, and other files). 

Big data sets are often unstructured data (videos, images, raw document files) that offer an opportunity for developing machine learning algorithms and data mining initiatives. But many relational databases cannot handle these large amounts of data - you need a data lake. 

Key features of Snowflake

1. Multiple storage use cases. Snowflake can be used as a data lake (supports unstructured data storage), as multiple data warehouses, and even as a database. The universality of this data storage architecture is unprecedented. 
2. Massively Parallel Processing (MPP). Snowflake processes SQL queries using MPP (massively parallel processing) compute clusters where each node in the cluster stores a portion of the entire data set locally. Making your SQL queries run extremely fast
3. Cloud-agnostic. Snowflake - unlike BigQuery on Google Cloud Platform, Microsoft Azure, Amazon Redshift on Amazon Web Service (AWS), or other data warehousing solutions - can run on any cloud provider. 

Runner-ups for best big data tool for data storage

• Apache Hive. Hive is a distributed, fault-tolerant data warehouse system that facilitates reading, writing, and managing petabytes of data residing in distributed storage using SQL. Apache Hive stores data using Apache Hadoop Distributed File System (HDFS).
• NoSQL databases. For niche and complex data use cases it is better to use a NoSQL database such as Apache Cassandra, MongoDB, or Neo4j. Check when it is best to use a NoSQL database for your big data storage.

Best big data tool for machine learning: Python

Some might argue that Python is not a big data tool, but a programming language, like Scala or Java. But unlike Scala or Java, Python comes with an ecosystem of machine learning algorithms and libraries that make the life of data scientists easier. 

Key features of Python

1. User-friendly. Unlike many other programming languages, Python’s syntax and architecture are easy to pick up and use. 
2. Data science libraries. From PyTorch and OpenCV for computer vision to scikit-learn for more traditional machine learning and predictive modeling, Python offers the greatest number of libraries that help you build machine learning data models.
3. Data analysis and data mining. Python is not just great for predictive analytics, it is also a versatile tool for data exploration and data analysis. For example, the Apache Spark framework helps you quickly wrangle large data volumes into a form suitable for data science.

Runner-ups for best big data tool for machine learning

• R. The programming language offers more out-of-the-box solutions for time series forecasting compared to Python. 
• AutoML. Google’s AutoML allows you to train custom machine learning models with minimal effort - the big data tool takes care of the logic and complexity for you.

Best big data tool for data analytics and data visualization: Tableau

Use your data for better decision-making with the right business intelligence tool. Tableau is the best big data tool for business intelligence that helps you automate data visualizations and data analytics. 

Key features of Tableau

• Beautiful and practical data visualizations. Tableau allows you to build and customize dashboards and visualizations that quickly explain the story behind the data. An added benefit: Tableau’s dashboards are interactive, making them more appealing to data consumers.
• Scales seamlessly. Tableau can compute metrics and generate reports over large datasets without compromising the speed of data visualization updates. A necessary feature when visualizing large volumes of data.
• User friendly. Tableau is designed with the no-coder in mind. It can easily be picked up by technical and non-technical profiles alike, offering an intuitive user experience. 

Automate data preparation for Tableau with Keboola’s integration. Keboola helps you prepare the large datasets with a couple of clicks. So you can spend more time analyzing the insights from Tableau. 

Runner-ups for best big data tool for data visualization

The biggest contender for the best big data tool for business intelligence is PowerBI. A well-known name among business intelligence apps, PowerBI offers a powerful analytics platform that can easily be extended and integrated via APIs and scales with large data volumes. PowerBI would be the go-to big data analytics tool if it weren’t for its deployment abilities. It only runs on Windows and works best with a Microsoft environment.

Pick Keboola to bring all the best big data tools under a single roof

Keboola is not just great for big data processing, it’s the go-to data stack as a service platform. 

Keboola automates all the data operations across your machine learning lifecycle to make your life easier:

• Build and automate end-to-end your data pipelines with over 250 pre-built connectors.
• Store and model data in the data lake of your choice (Snowflake, NoSQL, Azure data lake, AWS Redshift, Google’s BigQuery, etc.). 
• Transform and process the data with your favorite tools (Python, R, Julia, SQL, or dbt) and their machine learning libraries.
• Send the data to the business intelligence tool of your choice.

Keboola can help you turn your big data into a successful project. Check the platform for yourself (no credit card required). 

Try Keboola for free.

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