Real-Time Data Processing Technologies
Digital Processing Systems apply real-time data processing technologies for real-quick data insights and real-quick actionable results. Learn more about our real-time data processing capabilities in Flink, RabbitMQ, Storm, Spark Streaming, Kinesis, azure event hubs, azure stream analytics, Flume, Nifi, Samza, kafka and beyond.
Real-time Data Processing Technologies
In today fast-paced digital environment, data is the most valuable asset of a business. It can make or break your business and help navigate a successful business journey if dealt with appropriately. Companies need to remain agile, responsive, and up to date with data in real-time to keep pace with emerging new trends, tools, and technologies and stay afloat with market changes. Otherwise, they can risk losing their business value, reputation, efficiency, productivity, and ROI. Real-time data processing is integral to strategic decision-making; we help enterprises access cutting-edge real-time data processing technologies for accelerated business outcomes on time.
Real-time data processing technologies have been through a tremendous transition and transformation in the past. They are still undergoing continuous improvements and upscaling to meet new-age real-time data processing and digital challenges. Today, users want data transparency, accuracy, and precise responses in real-time to extract the maximum value from the data and predict the future. With billions of digital devices worldwide and infinite data available on the internet, real-time data processing has become a need of the hour if you expect to stay atop your business with a competitive edge.
Real-Time Data Processing Technologies
Real-time data holds a significantly high-value for enterprises looking to seek competitive edge and business efficiency through real-time data processing. Numerous businesses and digital sectors worldwide rely entirely on data insights and how data is being processed in real-time from potentially large volumes of datasets and sources. Some of the examples of real-time processing include aircraft control, spacecraft control, heart rate monitoring, traffic lights, e-commerce purchases, social networks, financial stocks and trading, online booking and reservations, banking transactions, health monitoring, ATM withdrawals and fraud detection, web and mobile application and more.
Our robust capabilities in Machine Learning, data science technologies, data analysis tools, BI tools and big data help us implement a real-time intelligent data processing system for ultra-quick real-time acquisition, processing, analysis and decision-making. Through analyzing and evaluating real-time data streams and use cases, you can gain actionable insights to fine-tune your business operations, improve business agility, improve customer service, make better-informed decisions and quickly respond to any crisis and opportunity that may come your way to a dynamic future.
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List of Real-Time Data Processsing Technologies
We help data-driven businesses to build high-performance, distributed and data-accurate, and data-powered streaming applications. Flink is pretty useful in real-time processing and as a batch processing framework, but it prioritizes streaming first as it is more stream-oriented than Storm and Spark. The best part about the Flink is it can be easily integrated with several other open-source data processing ecosystems.
Apache Spark Streaming
Spark Streaming is an open-source real-time analytics and data streaming tool, which is an extension of the core Spark API that allows data scientists and data engineers to process real-time data from multiple sources, including but not limited to Amazon Kinesis, Flume, Kafka. Apache Spark Streaming natively supports streaming and batch workloads and provide fast recovery from stragglers and failures since it is a scalable fault-tolerant streaming processing system.
Azure Stream Analytics
Microsoft Azure stream analytics is a highly-scalable, server-less, and real-time complex event processing engine that enables and empowers users to build, and run real-time data analytics on different data streams from multiple sources like sensors, social media, devices, websites, and other applications. We enable users to set up alerts to predict trends, detect anomalies, trigger necessary observation-driven workflows and mission-critical workloads, and make data available and easily accessible to other downstream services and archiving applications, presentation or further analysis.
Azure Event Hubs
Azure Event Hubs is an event ingestion service and big data streaming platform that can receive and process tens of thousands or even millions of event per second. Data sent or transferred to an Azure Event hub can be transformed, converted, and stored through any real-time batching/storage adapters and analytics providers. The use cases and scenarios for Azure Event Hub include application lodging, archiving data, live dashboarding, anomaly detection, user telemetry processing, device telemetry streaming, transaction processing and more. We provide you end-to-end support across all Azure Event Hubs verticals.
Kafka stream is a client-oriented library for writing and building mission-critical apps and microservices, where the input and output data and information are stored in Kafta clusters. It combines the power and simplicity of writing, developing and deploying standard Scala and Java applications on the client-side with Kafka’s server-side cluster technology advantages. Kafka stream is elastic, fault-tolerant and highly scalable and can be deployed to Cloud, containers, bare metal, and VMs. It doesn’t require any separate processing and can be developed on Linux, Mac, and Windows. On top of everything, it is fully-integrated with Kafka security and equally viable for small-scale, medium-scale and large use cases.
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Real-Time Data Processing And Streaming Tools
No enterprise would want to compromise with data as it is the backbone of running a smooth business. Therefore, the popularity and demand for data analytics and real-time data processing and streaming tools are increasing substantially among tech startups and business enterprises worldwide. The rapid transition of enterprises to the cloud computing landscape is the real reason why real-time data processing and streaming tools have become significantly important today. Here are some of the real-time data processing tools that can help improve data pipelines’ agility and speed for different applications.
Flume is reliable, distributed, a high-connectivity-driven system for efficiently and accurately collecting, accumulating, and moving large volumes of data from multiple sources (email messages, log files, social media, network traffic, etc.) single and centralized data store such as HBase or HDFS. We implement this data ingestion tool because it has a flexible and simple architecture based on streaming data flows and provides reliability mechanism for failure recovery and fault tolerance. It is also cross-compatible with Apache Hadoop that can store all kinds of data, making Flume an integral part of the Hadoop ecosystem. The best part about Flume is that it complements other data processing tools such as Kafka co-creating value with other platforms at par and beyond.
Apache NiFi is also one of the most common used tools for real-time data processing. We help you move or transport data between disparate sources, systems using this integrated data logistic platform. It provides greater flexibility and real-time control, making it easy to channelize and manage the movement of high-value data between any destination and source. Apache NiFi, a real-time data processing tool, is most widely used by courier delivery businesses such as FedEx, Leopards and other delivery services, allowing you to trace your data in real-time, just like your trace your package or delivery.
Apache Kafka is the most-widely-used open-source real-time stream-processing technology for accumulating, storing, evaluating data at scale. We help you leverage this software’s expected benefits as it is known for its remarkable performance, high throughput, fault tolerance, and low latency features. It is quite powerful and capable of handling and managing tens of thousands of messages per second. Using this robust and high-value platform, we help every data-focused digital sector leverage real-time data streams, building data pipelines, enabling data integration and operational metrics across infinite sources. We help enterprises to be upscale and modernize their data-centric strategies with even streaming architecture through this trusted tool.
Apache Storm is an open-source, distributed, and fault-tolerant computation system and real-time data processing tool capable of processing data stream ridiculously fast. There are widespread uses cases for Apache Storm, including but not limited to real-time analytics, distributed RPC, ETL, continuous computation, online machine learning. Unlike Hadoop platform that relies on batch processing, Apache Strom, built by Twitter, is designed explicitly for flowing data streams. We have lasting experience in Apache Storm to help you drive maximum value in a real-time data processing ecosystem.
Amazon Kinesis is yet another real-time, scalable, and fully-managed data processing tool that enables you to collect, ingest, buffer, process, analyze streaming data in real-time, so you can get timely insights and respond quickly to new information. Our excellence in this platform allows you to derive actionable data insights in seconds or minutes instead of hours and days. With powerful Kinesis, we help businesses build real-time live streaming applications using SQL editor, and Java libraries and help companies get rid of the stress and hassle of managing, and optimizing servers and other complexities vis-à-vis building and managing apps for real-time processing.
Apache Samza is a popular stream processing framework that provides high-level state storage, fault tolerance and buffering. From characteristics of high-performance, easy-to-operate, and horizontally scalable to powerful APIs, pluggable architecture and ‘write once, run everywhere feature’, Samza’s list of salient features is extensive. We use this robust distributed stream processing framework to build high-performance, fully-functional and interactive apps that process data in real-time from different sources, including but not limited to Apache Kafka. Apache Samza is battle-tested at speed and scale as it supports easy and flexible deployment options to run as a standalone library or on YARN.