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Description
Apache Accumulo enables users to efficiently store and manage extensive data sets across a distributed cluster. It relies on Apache Hadoop's HDFS for data storage and utilizes Apache ZooKeeper to achieve consensus among nodes. While many users engage with Accumulo directly, it also serves as a foundational data store for various open-source projects. To gain deeper insights into Accumulo, you can explore the Accumulo tour, consult the user manual, and experiment with the provided example code. Should you have any inquiries, please do not hesitate to reach out to us. Accumulo features a programming mechanism known as Iterators, which allows for the modification of key/value pairs at different stages of the data management workflow. Each key/value pair within Accumulo is assigned a unique security label that restricts query outcomes based on user permissions. The system operates on a cluster configuration that can incorporate one or more HDFS instances, providing flexibility as data storage needs evolve. Additionally, nodes within the cluster can be dynamically added or removed in response to changes in the volume of data stored, enhancing scalability and resource management.
Description
Spark Streaming extends the capabilities of Apache Spark by integrating its language-based API for stream processing, allowing you to create streaming applications in the same manner as batch applications. This powerful tool is compatible with Java, Scala, and Python. One of its key features is the automatic recovery of lost work and operator state, such as sliding windows, without requiring additional code from the user. By leveraging the Spark framework, Spark Streaming enables the reuse of the same code for batch processes, facilitates the joining of streams with historical data, and supports ad-hoc queries on the stream's state. This makes it possible to develop robust interactive applications rather than merely focusing on analytics. Spark Streaming is an integral component of Apache Spark, benefiting from regular testing and updates with each new release of Spark. Users can deploy Spark Streaming in various environments, including Spark's standalone cluster mode and other compatible cluster resource managers, and it even offers a local mode for development purposes. For production environments, Spark Streaming ensures high availability by utilizing ZooKeeper and HDFS, providing a reliable framework for real-time data processing. This combination of features makes Spark Streaming an essential tool for developers looking to harness the power of real-time analytics efficiently.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Apache Corporation
Founded
1954
Country
United States
Website
accumulo.apache.org
Vendor Details
Company Name
Apache Software Foundation
Founded
1999
Country
United States
Website
spark.apache.org/streaming/