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Description
Materialize is an innovative reactive database designed to provide updates to views incrementally. It empowers developers to seamlessly work with streaming data through the use of standard SQL. One of the key advantages of Materialize is its ability to connect directly to a variety of external data sources without the need for pre-processing. Users can link to real-time streaming sources such as Kafka, Postgres databases, and change data capture (CDC), as well as access historical data from files or S3. The platform enables users to execute queries, perform joins, and transform various data sources using standard SQL, presenting the outcomes as incrementally-updated Materialized views. As new data is ingested, queries remain active and are continuously refreshed, allowing developers to create data visualizations or real-time applications with ease. Moreover, constructing applications that utilize streaming data becomes a straightforward task, often requiring just a few lines of SQL code, which significantly enhances productivity. With Materialize, developers can focus on building innovative solutions rather than getting bogged down in complex data management tasks.
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
Integrations
Activeeon ProActive
Amazon Kinesis
Amazon S3
Apache Kafka
Apache Spark
DataClarity Unlimited Analytics
Debezium
Gravity Data
Hue
Orb
Integrations
Activeeon ProActive
Amazon Kinesis
Amazon S3
Apache Kafka
Apache Spark
DataClarity Unlimited Analytics
Debezium
Gravity Data
Hue
Orb
Pricing Details
$0.98 per hour
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
Materialize
Country
United States
Website
materialize.com
Vendor Details
Company Name
Apache Software Foundation
Founded
1999
Country
United States
Website
spark.apache.org/streaming/
Product Features
Data Warehouse
Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge
Database
Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization
Streaming Analytics
Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards