With the vast amount of data generated by the world, the need for an efficient and accurate platform and tool to manage, analyze, and extract value from data is increasing. In 2025, many companies will rely on advanced data platforms and data analytics tools to manage and store data and make data-driven decisions to improve operational efficiency. The data platform and tools help businesses to handle data storage and processing for analysis. Here are the top 10 data platforms and tools for 2025 that can help businesses maintain their data, make accurate decisions based on them, streamline workflow, etc.
Google Cloud Platform is a suite of data storage, processing and analytics services that includes BigQuery for large-scale data analytics and various tools for machine learning, AI, and management of data. Rather than managing the physical data, users can use the GCP’s resources on a pay-as-you-go basis to scale their operations and reduce costs.
It is a comprehensive cloud platform offered by Amazon that offers a wide range of on-demand services like data storage, computing power, networking, databases, machine learning, analytics, etc. The AWS allows the business enterprise to rent the computing resources to maintain their hardware and software. Its data services like Amazon S3 for storage, Amazon Redshift for data warehousing, and AWS Lambda for serverless processing make it the leading choice for businesses having large data scales.
Offered by Microsoft, this cloud computing platform offers a wide range of on-demand services that include computing power, artificial intelligence, data analytics, storage, database, networking, etc. to help businesses build and manage applications with Microsoft’s global network of data centres. It has services like Azure Data Lake, Azure SQL Database, and Power BI for managing and analysing data with the integration of cloud-based applications.
The Apache Hadoop is a well-known open-source platform that is used to process large datasets across multiple computers. It is a cost-effective solution that helps in the storage and analysis of both structured and unstructured data.
Its Hadoop Distributed File System stores data across various machines for better availability and reliability and the MapReduce processing engine helps in parallel data processing across the cluters. Companies like Yahoo, Facebook, and Twitter use Hadoop to manage large volumes of data efficiently.
Apache Spark is one of the powerful analytics engines known for its speed by performing data processing in-memory and processing a large amount of data that includes batch processing, real-time streaming, machine learning, graph processing, etc.
Spark also supports multiple programming languages like Java, Scala, Python, and R making it helpful for the developers. It has a wide library of tools like Spark SQL for querying structured data, MLib for machine learning, and GraphX for processing graphs. Spark also works seamlessly with Hadoop to help organizations build their existing big-data infrastructure. Renowned companies like Netflix, Airbnb, and Uber use Apache Spark.
Databricks is one of the most powerful big-data platforms that is developed on Apache Spark and designed to simplify the creation and deployment of big-data applications. It provides a scalable and fully managed infrastructure that will help in real-time data processing, advanced analytics, and machine learning. It has an interactive workspace for writing codes, visualisation of data and work collaboration; the autoscaling ability helps in large datasets and complex task handling. Renowned companies using Databricks are Nvidia, Johnson & Johnson, and Salesforce.
Google Cloud BigQuery is one of the top-rated big data platforms that help in fully managed and serverless data warehouse solutions. It allows the user to store, query, and analyse large datasets at a petabyte scale with high speed and efficiency. This tool integrates well with other Google Cloud Services that feature automatic scaling. It supports SQL queries, built-in machine learning, and geospatial analytics. Companies that use these tools include Spotify, Walmart, and the New York Times.
Tableau is one of the leading data visualization tools that is used in business analytics and it offers a simple interface that helps in connecting various data sources, allowing the users to develop interactive dashboards and reports. It is a simple tool for business users, however, data analytics can also use its advanced features for tasks like clustering and regression. Coca-Cola, Skype, and Tata 1mg use Tableau to analyze and visualize data, and to communicate data-related insights.
Google Analytics is one of the most widely used tools that helps businesses understand the user’s behaviour and how they interact with their websites and web pages. To use this, the user needs to add a special Javascript code to the web pages as the code will collect all the user’s information like time spent on the particular page, its bounce rate, how many times they have clicked on the webpage, which page they visit the mosts, etc. It then sends the data to GA where it organizes and converts it into reports for the user to analyse which page is bringing traffic to the website or how is the user behaving with the web pages.
Apache Kafka is one of the top big data analytics tools that is designed for developing real-time data pipelines and applications. It was developed by LinkedIn and is a part of the Apache project, this tool is known for its high throughput, fault tolerance, and scalability for which it is the most efficient tool to handle millions of messages per second without downtime. It supports the real-time processing that enables the business to analyze data and act as soon as it arrives. It also ensures durability by storing data streams for later use. LinkedIn, Yahoo, Twitter, and Tesla use Apache Kafka as their big data analytics tool.
2025 will be the year of Artificial Intelligence and Technology advancement and thus it will be essential for businesses to stay competitive with perfect data storage, management, and analysis to optimise the daily operational tasks.