Arenadata Hadoop

In development. HUE (Hadoop User Experience) is a web-based interface for the Hadoop ecosystem for data analytics.

Hue allows users to perform data analysis without losing any context. The goal is to promote self service and stay simple like Excel so users can find, explore, query and analyze data. One of the main advantages of Hue is the ability to connect to various data sources: Apache Hive, Impala, Flink SQL, Spark SQL, Phoenix, ksqlDB, Apache Hadoop HDFS, Ozone, HBase, etc.

In development. Apache Ozone is an open-source, scalable, and distributed object store designed for big data workloads. It is part of the Apache Hadoop ecosystem and is built on top of Hadoop Distributed File System (HDFS).

Ozone is designed to provide high performance and scalability for storing and processing large amounts of unstructured data such as log files, images, videos, and other large data objects. It is optimized for workloads that require high throughput and low latency, such as big data analytics, machine learning, and streaming data processing.

One of the key features of Ozone is its support for multiple storage classes, including hot, warm, and cold storage. This allows users to store data based on its access patterns and lifecycle, optimizing cost and performance.

Ozone also includes built-in data replication and distribution capabilities, enabling data to be stored across multiple nodes in a Hadoop cluster for improved availability and durability.

Apache Kyuubi is a distributed and multi-tenant gateway to provide SQL on Data Warehouses and Lakehouses.

Kyuubi builds distributed SQL query engines on top of various kinds of modern computing frameworks, e.g. Apache Spark, Flink, Hive, Impala, etc., to query massive datasets distributed over fleets of machines from heterogeneous data sources.

Apache Impala is an open-source massively parallel processing (MPP) SQL query engine for processing large volumes of data in real-time. It allows users to perform interactive queries on Apache Hadoop data stored in HDFS or Apache HBase. Impala was developed to address the need for a faster, more efficient SQL query engine for big data processing than traditional batch-oriented SQL engines.

Impala provides high-speed performance through its MPP architecture, which enables it to distribute processing across multiple nodes in a Hadoop cluster. It also includes support for advanced features such as complex joins, subqueries, and aggregation functions.

Impala is designed to be easy to use and integrate with existing BI and analytics tools. It supports standard SQL queries and JDBC/ODBC drivers for easy integration with a wide range of applications.

Apache ZooKeeper is a distributed coordination service that is designed to help manage large distributed systems. It provides a centralized infrastructure for maintaining configuration information, naming, providing distributed synchronization, and providing group services. ZooKeeper is used extensively in Hadoop clusters to help manage the coordination of distributed systems and to ensure that each node in the cluster is aware of the state of the other nodes.

HDFS is a highly scalable and fault-tolerant distributed file system that forms the foundation of the ADH platform. It allows you to store large volumes of data across multiple nodes in a cluster, with built-in redundancy to ensure that data is always available, even in case of a node failure. HDFS is optimized for handling large files, making it an ideal choice for big data applications.

YARN is a resource management and job scheduling framework that allows you to run multiple applications simultaneously on a Hadoop cluster. YARN enables you to allocate cluster resources dynamically, based on the needs of each application, and to monitor and manage those resources to ensure optimal performance.

This is a NoSQL database that provides real-time read/write access to large datasets stored in Hadoop. HBase is designed to handle massive volumes of data and is optimized for random, real-time access to data, making it a popular choice for big data applications that require low-latency access to large datasets.

Apache Phoenix is an open-source, SQL-like query engine for Hadoop that is designed to provide fast and efficient querying of large datasets. Phoenix is built on top of HBase, which means that it can handle massive amounts of data with low latency and provides support for real-time updates and access to data.

Apache Spark is a fast and powerful open-source data processing engine that provides scalable, fault-tolerant data processing capabilities for big data workloads. The Apache Spark component of Arenadata Hadoop provides a high-performance and distributed computing framework that can process large datasets in parallel across a cluster of nodes. With its advanced analytics capabilities, including machine learning, graph processing, and SQL-like querying, Apache Spark can help businesses extract valuable insights from their data.

Apache Hive is an open-source data warehouse infrastructure that provides data summarization, query, and analysis capabilities for large datasets stored in Hadoop. The Apache Hive component of Arenadata Hadoop provides a SQL-like interface for querying data in Hadoop, enabling businesses to perform ad-hoc queries, data analysis, and reporting. Hive translates SQL queries into MapReduce jobs, which can be executed on a Hadoop cluster. With its support for partitioning, indexing, and compression, Hive can help businesses optimize data storage and processing in Hadoop.

Apache Tez is an open-source data processing framework that provides a flexible, efficient, and scalable way to execute complex data processing tasks on a Hadoop cluster. When used together with Apache Hive, Tez provides a faster and more efficient way to execute Hive queries, by replacing the MapReduce execution engine with a more optimized one. The Hive + Tez combination in Arenadata Hadoop provides a powerful and scalable platform for data warehousing, allowing businesses to perform ad-hoc queries, data analysis, and reporting at scale. With Tez's support for dynamic task scheduling and data partitioning, it can accelerate query processing by optimizing the data flow between Hive operators.

Apache Flink is an open-source stream processing framework that enables the processing of large volumes of real-time data with low latency. The Apache Flink component of Arenadata Hadoop provides a distributed computing framework for real-time data processing that can be seamlessly integrated with batch processing. Flink supports event-driven processing and provides a unified programming model for both batch and stream processing, making it ideal for building end-to-end data processing pipelines. With its advanced features, including support for stateful streaming, windowing, and machine learning, Apache Flink can help businesses gain real-time insights from their data.

Apache Solr is an open-source, enterprise-level search platform that is built on top of the Apache Lucene search library. Solr provides a robust and scalable search solution that is used by organizations of all sizes to power search functionality on their websites, mobile apps, and other applications.