WSO2 Stream Processor and Analytics Server

The WSO2 Stream Processor and Analytics Server is a is a cloud-based ingestion and processing system. It is designed to capture, process and analyze big data in real time.

When working with big datasets, as is common with direct marketing organizations, managing success of campaigns within real time can help identify a number of factors which can help best maximize profit margins.

As opposed to similar products on the market which tend to be resource-heavy, WSO2 Stream Processor has a considerably lighter weight. Instead of the multi-node deployments typically required with most stream-processing systems, WSO2 requires only two community nodes, making it much easier to manage and make changes in very short time periods. Its cloud-based and highly scalable nature makes it easy to deploy and manage in multiple locations.

Data from WSO2 Stream Processor system can be easily queried using streaming SQL queries so as to provide real information that can be easily interpreted and acted upon depending on business needs. The Analytics Server can process and display information in easy-to-understand formats.

 

Data Extraction

The WSO2 Analytics server includes a predefined real-time analytics platform which, when deployed, will process an existing data source. With a few quick clicks, this will automatically analyze the data according to a few set specifications. There are several data visualization options to choose from to gain a clearer picture of data being extracted and analyzed.

Rapid Data Processing

The WSO2 Stream Processor has the ability to process millions of events per second allowing the analytics server to handle 100,000 events per-second. Few big data real-time web analytics tools have this capacity. If using the Apache Kafka Web Service, this can be scaled to other locations as well. For large direct mail marketing operations, this is one of the most powerful open source big data analytics engines available.

Scalability

The WSO2 Analytics Server uses a distributed architecture making it highly scalable. While the system only requires two nodes to deploy, using the Apache Kafka web service makes it easy to scale up to many as needed. It is very container-friendly, making it possible to build new nodes whenever and wherever necessary. If a direct mail marketing organization needs to set up locations in various parts of the globe, the WSO2 Stream Processor makes it possible to expand with very little pain to the operation. Considering that two nodes can handle 100,000 processes/second, by adding more, throughput can increase exponentially. Considering how fast new data can become available, the ability to scale up can become extremely valuable.

 

 

Data Visualization

One of the features of the WSO2 Analytics Server that direct marketers will appreciate most is a customizable dashboard portal, which will provide a high-level view of current activity. The dashboard allows data to be displayable in tabular or various graph formats. It allows customization into multiple views, depending on need and purpose. Live streams can be accompanied with more static data. For instance , if one is mailing to a region where there might be extreme weather, or anything else that might affect delivery or response rates, this information can be displayed in real-time. Visualization also makes it possible to identify things that might not easily be spotted in raw data formats.

 

 

Multiple Data Sources and Views

WSO2 Analytics server and Stream Processor supports different views for different users. For example, if data is needed for delivery staff, upper-level, and lower-level management, the data can be partitioned to deliver only the necessary information for each business segment. Different dashboards can also be configured accordingly. One way that the this tool differs from its competitors is that can handle data from different database types, including h2, MySQL, PostgreSQL, Oracle and MSSQL. Using their Siddhi platform it will translate data from a long list of source types including the following and allow streaming SQL querying.

It accepts the following source types:

• HTTP
• Kafka
• TCP
• In-memory
• WSO2Event
• Email
• JMS
• Prometheus

• File
• RabbitMQ
• MQTT
• WebSocket
• Twitter
• Amazon SQS
• CDC

Mapping types supported:

• WSO2Event
• XML
• TEXT
• JSON

• Binary
• Key Value
• CSV
• Avro

Advanced Data Security:

The WSO2 Analytics server and Stream Processor provides several layers of security in the user's platform as well as the cloud server, including many anti-hacking, anti-phishing, and anti-virus features.

APIs can be monitored from the WSO2 Analytics platform to detect any irregularities with status. Alerts can be set up for certain conditions which can be sent via email or SMS, and/or displayed on a dashboard. The identity server can also be monitored in similar ways, to detect number logins, and unauthorized login attempts.

Deployment

Deployment is relatively simple. Many other analytical tools with similar capabilities would require a considerable amount of hosted hardware. The WSO2 Stream processor only requires 2 CPUs and 4GB of memory, and a minimum of 5GB disk space for the necessary nodes, 3 Kafka brokers and Zookeeper nodes, 2 RDBMS Nodes and an Internet Connection. Hosting the WSO2 Analytics Server and Stream Platform can be done in the cloud, and can be deployed in multiple locations with very little expense. As mentioned earlier, it is very container-friendly, so replication becomes relatively painless.

The WSO2 Analytics server and Stream Processor is one of the most advanced real-time data analytics tools in the market today.

Along with the power provided by streaming SQL and the valuable information provided by its customizable data visualization dashboard, the WSO2 Analytics server and Stream Processor is very user friendly, in ways that other applications of this type are not.

Given that it is lightweight and can be deployed in multiple locations and can be scaled easily while consuming minimal resources, makes this data ingestion and visualization a good choice for direct marketers to gain a high-level view of their sales and distribution operations.

• MySQL
• Oracle
• MSSQL
• Postgres
• MongoDB
• Cassandra
• Redis
• Hazelcast
• Solr
• Elasticsearch
• 60+ extensions
• Connectors for Kafka, JMS, Rabbitmq, MQTT, Amazon SQS, web-socket.