Data Stream Technologies

Data Stream Technologies. Apache NIFI is another RealTime Data Streaming It has integrated data logistics features which make it the platform for automating the data movement between different sources and destinations NIFI also supports the distributed sources which can be like files social feeds log files and videos etc.

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4 Key Components of a Streaming Data Architecture (with

What Is Streaming Data and Streaming Data Architecture?Why Streaming Data Architecture? Benefits of Stream ProcessingThe Components of A Streaming ArchitectureModern Streaming ArchitectureThe Future of Streaming DataFurther ReadingStreaming data refers to data that is continuously generated usually in high volumes and at high velocity A streaming data source would typically consist of a stream of logs that record events as they happen – such as a user clicking on a link in a web page or a sensor reporting the current temperature Common examples of streaming data include 1 IoT sensors 2 Server and security logs 3 Realtime advertising 4 Clickstream data from apps and websites In all of these cases we have end devices that are continuously generating thousands or millions of records forming a data stream – unstructured or semistructured form most commonly JSON or XML keyvalue pairs Here’s an example of how a single streaming event would look – in this case the data we are looking at is a website session A single streaming source will generate massive amounts of these events every minute In its raw form this data is very difficult to work with as the lack of schema and structure makes it diffic Stream processing used to be a niche technology used only by a small subset of companies However with the rapid growth of SaaS IoT and machine learning organizations across industries are now dipping their toes into streaming analytics It’s difficult to find a modern company that doesn’t have an app or a website as traffic to these digital assets grows and with the increasing appetite for complex and realtime analytics the need to adopt modern data infrastructure is quickly becoming mainstream While traditional batch architectures can be sufficient at smaller scales stream processing provides several benefits that other data platforms cannot 1 Able to deal with neverending streams of events—some data is naturally structured this way Traditional batch processing tools require stopping the stream of events capturing batches of data and combining the batches to draw overall conclusions In stream processing while it is challenging to combine and capture data from mult Most streaming stacks are still built on an assembly line of opensource and proprietary solutions to specific problems such as stream processing storage data integration and realtime analytics At Upsolver we’ve developed a modern platform that combines most building blocks and offers a seamless way to transform streams into analyticsready datasets You can check out our technical white paperfor the details Whether you go with a modern data lake platform or a traditional patchwork of tools your streaming architecture must include these four key building blocks In modern streaming data deployments many organizations are adopting a full stack approach rather than relying on patching together opensource technologies The modern data platform is built on businesscentric value chains rather than ITcentric coding processes wherein the complexity of traditional architecture is abstracted into a single selfservice platform that turns event streams into analyticsready data The idea behind Upsolver is to act as the centralized data platform that automates the laborintensive parts of working with streaming data message ingestion batch and streaming ETL storage management and preparing data for analytics Benefits of a modern streaming architecture 1 Can eliminate the need for large data engineering projects 2 Performance high availability and fault tolerance built in 3 Newer platforms are cloudbased and can be deployed very quickly with no upfront investment 4 Flexibility and support for multiple use cases Here’s how you would u Streaming data architecture is in constant flux Three trends we believe will be significant in 2022 and beyond 1 Fast adoption of platforms that decouple storage and compute—streaming data growth is making traditional data warehouse platforms too expensive and cumbersome to manage Data lakes are increasingly used both as a cheap persistence option for storing large volumes of event data and as a flexible integration point allowing tools outside the streaming ecosystem to access streaming data 2 From table modeling to schemaless development—data consumers don’t always know the questions they will ask in advance They want to run an interactive iterative process with as little initial setup as possible Lengthy table modeling schema detection and metadata extraction are a burden 3 Automation of data plumbing—organizations are becoming reluctant to spend precious data engineering time on data plumbing instead of activities that add value such as data cleansing or enrichm You can read more of our predictions for streaming data trends here to see how many of them we got right or check out some other articles we’ve written about cloud architecture as well as other streaming datatopics Want to build or scale up your streaming architecture? Upsolver is a streaming data platform that processes event data and ingests it into data lakes data warehouses serverless platforms Elasticsearch and more making SQLbased analytics instantly available Upsolver also enables real time analytics using lowlatency consumers that read from a Kafka stream in parallel It is a fully integrated solution that can be set up in hour Schedule a demo to learn how to build your nextgen streaming data architecture or watch the webinarto learn how it’s done.

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Flink Apache Flink is a streaming data flow engine which aims to provide facilities for distributed computation over streams of data Treating batch processes as a special case of data streaming Flink is effective both as a batch and realtime processing framework but it puts streaming first Storm Apache Storm is a distributed realtime computation system Its applications are designed as directed acyclic graphs Storm can be used with any programming language Kinesis Kafka and Kinesis are very similar Although Kafka is free and requires you to make it into an enterpriseclass solution for your organization Samza Apache Samza is another distributed stream processing framework which is tightly tied to the Apache Kafka messaging system Samza is designed specifically to take advantage of Kafka’s unique architecture and guarantees fault tolerance buffering and state storage.