Best Streaming Analytics Platforms for Apache Iceberg

CelerData is an advanced SQL engine designed to enable high-performance analytics directly on data lakehouses, removing the necessity for conventional data warehouse ingestion processes. It achieves impressive query speeds in mere seconds, facilitates on-the-fly JOIN operations without incurring expensive denormalization, and streamlines system architecture by enabling users to execute intensive workloads on open format tables. Based on the open-source StarRocks engine, this platform surpasses older query engines like Trino, ClickHouse, and Apache Druid in terms of latency, concurrency, and cost efficiency. With its cloud-managed service operating within your own VPC, users maintain control over their infrastructure and data ownership while CelerData manages the upkeep and optimization tasks. This platform is poised to support real-time OLAP, business intelligence, and customer-facing analytics applications, and it has garnered the trust of major enterprise clients, such as Pinterest, Coinbase, and Fanatics, who have realized significant improvements in latency and cost savings. Beyond enhancing performance, CelerData’s capabilities allow businesses to harness their data more effectively, ensuring they remain competitive in a data-driven landscape.

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.

Apache Flink serves as a powerful framework and distributed processing engine tailored for executing stateful computations on both unbounded and bounded data streams. It has been engineered to operate seamlessly across various cluster environments, delivering computations with impressive in-memory speed and scalability. Data of all types is generated as a continuous stream of events, encompassing credit card transactions, sensor data, machine logs, and user actions on websites or mobile apps. The capabilities of Apache Flink shine particularly when handling both unbounded and bounded data sets. Its precise management of time and state allows Flink’s runtime to support a wide range of applications operating on unbounded streams. For bounded streams, Flink employs specialized algorithms and data structures optimized for fixed-size data sets, ensuring remarkable performance. Furthermore, Flink is adept at integrating with all previously mentioned resource managers, enhancing its versatility in various computing environments. This makes Flink a valuable tool for developers seeking efficient and reliable stream processing solutions.