List of the Top 5 Big Data Platforms for SingleStore in 2025

Google Cloud Platform (GCP) stands out in its ability to handle and analyze large-scale data through its advanced tools, such as BigQuery, which serves as a serverless data warehouse enabling rapid querying and analysis. Additional services like Dataflow, Dataproc, and Pub/Sub empower organizations to efficiently manage and analyze extensive datasets. New customers are welcomed with $300 in complimentary credits, allowing them to experiment, test, and implement workloads without immediate financial pressure, thereby speeding up their journey toward data-driven discoveries and innovations. With its robust and scalable infrastructure, GCP allows businesses to swiftly process vast amounts of data, ranging from terabytes to petabytes, all while keeping costs significantly lower than traditional data solutions. Furthermore, GCP's big data offerings are designed to seamlessly integrate with machine learning tools, providing a well-rounded ecosystem for data scientists and analysts to extract meaningful insights.

Looker revolutionizes business intelligence (BI) by introducing a novel data discovery solution that modernizes the BI landscape in three key ways. First, it utilizes a streamlined web-based architecture that depends entirely on in-database processing, allowing clients to manage extensive datasets and uncover the final value in today's fast-paced analytic environments. Second, it offers an adaptable development setting that enables data experts to shape data models and create tailored user experiences that suit the unique needs of each organization, thereby transforming data during the output phase instead of the input phase. Moreover, Looker provides a self-service data exploration experience that mirrors the intuitive nature of the web, giving business users the ability to delve into and analyze massive datasets directly within their browser interface. Consequently, customers of Looker benefit from the robust capabilities of traditional BI while experiencing the swift efficiency reminiscent of web technologies. This blend of speed and functionality empowers users to make data-driven decisions with unprecedented agility.

Starburst enables organizations to strengthen their decision-making processes by granting quick access to all their data without the complications associated with transferring or duplicating it. As businesses gather extensive data, their analysis teams frequently experience delays due to waiting for access to necessary information for evaluations. By allowing teams to connect directly to data at its origin, Starburst guarantees they can swiftly and accurately analyze larger datasets without the complications of data movement. The Starburst Enterprise version offers a comprehensive, enterprise-level solution built on the open-source Trino (previously known as Presto® SQL), which comes with full support and is rigorously tested for production environments. This offering not only enhances performance and security but also streamlines the deployment, connection, and management of a Trino setup. By facilitating connections to any data source—whether located on-premises, in the cloud, or within a hybrid cloud framework—Starburst empowers teams to use their favored analytics tools while effortlessly accessing data from diverse locations. This groundbreaking strategy significantly accelerates the time it takes to derive insights, which is crucial for businesses striving to remain competitive in a data-centric landscape. Furthermore, with the constant evolution of data needs, Starburst adapts to provide ongoing support and innovation, ensuring that organizations can continuously optimize their data strategies.

The Apache Hadoop software library acts as a framework designed for the distributed processing of large-scale data sets across clusters of computers, employing simple programming models. It is capable of scaling from a single server to thousands of machines, each contributing local storage and computation resources. Instead of relying on hardware solutions for high availability, this library is specifically designed to detect and handle failures at the application level, guaranteeing that a reliable service can operate on a cluster that might face interruptions. Many organizations and companies utilize Hadoop in various capacities, including both research and production settings. Users are encouraged to participate in the Hadoop PoweredBy wiki page to highlight their implementations. The most recent version, Apache Hadoop 3.3.4, brings forth several significant enhancements when compared to its predecessor, hadoop-3.2, improving its performance and operational capabilities. This ongoing development of Hadoop demonstrates the increasing demand for effective data processing tools in an era where data drives decision-making and innovation. As organizations continue to adopt Hadoop, it is likely that the community will see even more advancements and features in future releases.

Apache Spark™ is a powerful analytics platform crafted for large-scale data processing endeavors. It excels in both batch and streaming tasks by employing an advanced Directed Acyclic Graph (DAG) scheduler, a highly effective query optimizer, and a streamlined physical execution engine. With more than 80 high-level operators at its disposal, Spark greatly facilitates the creation of parallel applications. Users can engage with the framework through a variety of shells, including Scala, Python, R, and SQL. Spark also boasts a rich ecosystem of libraries—such as SQL and DataFrames, MLlib for machine learning, GraphX for graph analysis, and Spark Streaming for processing real-time data—which can be effortlessly woven together in a single application. This platform's versatility allows it to operate across different environments, including Hadoop, Apache Mesos, Kubernetes, standalone systems, or cloud platforms. Additionally, it can interface with numerous data sources, granting access to information stored in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and many other systems, thereby offering the flexibility to accommodate a wide range of data processing requirements. Such a comprehensive array of functionalities makes Spark a vital resource for both data engineers and analysts, who rely on it for efficient data management and analysis. The combination of its capabilities ensures that users can tackle complex data challenges with greater ease and speed.