Best Query Engines for Hadoop

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.

Impala offers rapid response times and accommodates numerous concurrent users for business intelligence and analytical inquiries within the Hadoop ecosystem, supporting technologies such as Iceberg, various open data formats, and multiple cloud storage solutions. Additionally, it exhibits linear scalability, even when deployed in environments with multiple tenants. The platform seamlessly integrates with Hadoop's native security measures and employs Kerberos for user authentication, while the Ranger module provides a means to manage permissions, ensuring that only authorized users and applications can access specific data. You can leverage the same file formats, data types, metadata, and frameworks for security and resource management as those used in your Hadoop setup, avoiding unnecessary infrastructure and preventing data duplication or conversion. For users familiar with Apache Hive, Impala is compatible with the same metadata and ODBC driver, streamlining the transition. It also supports SQL, which eliminates the need to develop a new implementation from scratch. With Impala, a greater number of users can access and analyze a wider array of data through a unified repository, relying on metadata that tracks information right from the source to analysis. This unified approach enhances efficiency and optimizes data accessibility across various applications.

IBM Db2 Big SQL is a sophisticated hybrid SQL-on-Hadoop engine that facilitates secure and advanced data querying across a range of enterprise big data sources, such as Hadoop, object storage, and data warehouses. This enterprise-grade engine adheres to ANSI standards and provides massively parallel processing (MPP) capabilities, enhancing the efficiency of data queries. With Db2 Big SQL, users can execute a single database connection or query that spans diverse sources, including Hadoop HDFS, WebHDFS, relational databases, NoSQL databases, and object storage solutions. It offers numerous advantages, including low latency, high performance, robust data security, compatibility with SQL standards, and powerful federation features, enabling both ad hoc and complex queries. Currently, Db2 Big SQL is offered in two distinct variations: one that integrates seamlessly with Cloudera Data Platform and another as a cloud-native service on the IBM Cloud Pak® for Data platform. This versatility allows organizations to access and analyze data effectively, performing queries on both batch and real-time data across various sources, thus streamlining their data operations and decision-making processes. In essence, Db2 Big SQL provides a comprehensive solution for managing and querying extensive datasets in an increasingly complex data landscape.

A SQL query engine that operates without a predefined schema, designed for use with Hadoop, NoSQL databases, and cloud storage solutions. This innovative engine allows for flexible data retrieval and analysis across various storage types, adapting seamlessly to diverse data structures.

Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.

Palo empowers businesses to swiftly establish a PB-level MPP architecture data warehouse service in just minutes while seamlessly importing vast amounts of data from sources like RDS, BOS, and BMR. This capability enables Palo to execute multi-dimensional big data analytics effectively. Additionally, it integrates smoothly with popular BI tools, allowing data analysts to visualize and interpret data swiftly, thereby facilitating informed decision-making. Featuring a top-tier MPP query engine, Palo utilizes column storage, intelligent indexing, and vector execution to enhance performance. Moreover, it offers in-library analytics, window functions, and a range of advanced analytical features. Users can create materialized views and modify table structures without interrupting services, showcasing its flexibility. Furthermore, Palo ensures efficient data recovery, making it a reliable solution for enterprises looking to optimize their data management processes.