Real-Time Data Streaming Tools And Technologies

[markstarc]

Real-time data holds potentially high value for business but it also comes with a perishable expiration date. If the value of this data is not realized in a certain window of time, its value is lost and the decision or action which was needed as a result never occurs. Such data comes continuously and quite quickly, therefore, we call it streaming data. Data streaming requires special attention as sensor reading changing rapidly, blip in log file, sudden price change holds immense value but only if it alerted in time [url]Android app Development New York[/url] .

Although there are many technologies available, still while considering streaming in a data lake it is necessary to have a well-executed data lake which offers strict rules and processes in terms of ingestion.

Here are some real time data streaming tools and technologies.

1. Slink
Apache Slink 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, Link is effective both as a batch and real-time processing framework but it puts streaming first. Slink offers a number of APIs which includes static data API like Data-stream API, Data Set API for Java, Scala and Python and SQL-like query API for embedding in Java, Scala static API code. Link also has its own machine learning library called Linkmen, its own SQL Query called as well as graph processing libraries.

Compared to Spark and Storm, Slink is more stream-oriented. It is something of a hybrid between Spark and Storm. Spark operates in batch mode. Slink also provides a highly flexible streaming window for the continuous streaming model. This ensures that both batch and the real-time streaming gets integrated into one system.

Highlights
Highly Flexible Streaming Windows for Continuous Streaming Model.
Batch and Streaming in one system.
Slink is integrated with many other open-source data processing ecosystems.
2. Storm
Apache Storm is a distributed real-time computation system. Its applications are designed as directed cyclic graphs. Storm can be used with any programming language. It is known for processing over one million tuples per second per node which is highly salable and provides processing job guarantees. Storm is written in Closure which is the Lisp-like functional-first programming language.

Storm is used for distributed machine learning, real-time analytics, and numerous other cases, especially with high data velocity. Storm runs on YARN and integrates with Hardtop ecosystems. Storm is a stream processing engine without batch support, a true real-time processing framework, taking in a stream as an entire ‘event’ instead of series of small batches. Storm has low latency and is well-suited to data which must be ingested as a single entity. Storm does suffer from a lack of direct YARN support. Storm is a bridge between batch processing and stream processing, which Hardtop is not naively designed to handle.

Highlights
Storm is known for processing one million 100 byte msgs/sec/node.
It is salable which works on parallel calculations that run across a cluster of machines.
Storm is reliable. It guarantees that each unit of data (tuple) will be processed at least once or exactly once. Messages are only replayed when there are failures.
3. Kine sis
Kafka and Kine sis are very similar. Although Kafka is free and requires you to make it into an enterprise-class solution for your organization. But Amazon came to the rescue by offering Kine sis as an out of the box streaming data tool. Kine sis comprises of shards which Kafka calls partitions. For organizations that take advantage of real-time or near real-time access to large stores of data, Amazon Kine sis is great.

Kine sis Streams solves a variety of streaming data problems. One common use is the real-time aggregation of data which is followed by loading the aggregate data into a data warehouse. Data is put into Kine sis streams. This ensures durability and elasticity. Amazon Kine sis is a managed, salable, cloud-based service which allows real-time processing of large data streams.

Highlights
Kine sis is all about real-time data.
Kine sis Firehouse ingests real-time data into data stores like S3, Elastic search or Red-shift for batch analytics.
Kine-sis Analytics helps you to analyze data in real-time.
4. Samosa
Apache Samba is another distributed stream processing framework which is tightly tied to the Apache Kafka messaging system. Samosa is designed specifically to take advantage of Kafka’s unique architecture and guarantees fault tolerance, buffering and state storage.

Samosa uses YARN for resource negotiation. This means that by default, a Hardtop cluster is required and Samoa relies on rich features built into YARN. Samba is able to store state by using a fault-tolerant check pointing system which is implemented as a local key-value store. Therefore, this helps Samba to offer at least one delivery guarantee, though it does not offer reliability and accuracy of recovery of the aggregated state in the event of failure. It also offers high-level abstractions which in many ways is easier to work with than primitive options provided by systems like Storm. Samosa only supports JAM language which does not have the same language flexibility as Storm.

Highlights
Simple API: Samosa provides a very simple callback-based “process message” API as compared to Map-reduce.
Managed state: Samba manages snaps-hotting and restoration of stream processor’s state.
Fault tolerance: Samba works with YARN whenever a machine in the cluster fails in order to transparently migrate your tasks to another machine.
Sociability: Samosa is partitioned and distributed at all levels.
5. Kafka
Kafka is a distributed publish-subscribe messaging system which integrates applications/data streams. It was originally developed at LinkedIn Corporation and later became a part of Apache project. Therefore, Apache Spark is fast, salable and reliable messaging system which is the key component in Hardtop technology stack for supporting real-time data analytics or demonetization of Internet of Things (Io T) data.

Kafka can handle many terabytes of data without incurring much at all. Apache Kafka is altogether different from the traditional messaging system. It is designed as a distributed system and which is very easy to scale out.Kafka is designed to deliver three main advantages over AMP, DIMS etc.

Highlights
Highly Reliable: Kafka replicates data and it can support multiple subscribers. In the event of failure, it automatically balances consumers in the event of failure which is very much reliable in comparison to similar messaging services.
Superbly Salable: Kafka, which is a distributed system, is able to scale quickly and easily without incurring any downtime.
High Performance: For both publishing and subscribing, Kafka delivers high throughput. It is capable of offering constant levels of performance even when it deals with many terabytes of stored messages.
Durable: Kafka provides infra-cluster replication by keeping messages on the disks which make it durable messaging system.
Conclusion:
We have plenty of options for processing within a big data system. For stream-only workloads, Storm has wide language support and therefore can deliver very low latency processing. Kafka and Kine sis are catching up fast and providing their own set of benefits. For batch-only workloads which are not time-sensitive, Hardtop Map Reduce is a great choice.

For mixed kind of workloads, Spark offers high-speed batch processing and micro-batch processing for streaming. Link is also becoming popular and is positioned as an alternative to Spark. Of course, the best fit for your situation will depend a lot on the state of the data to process, your infrastructure preference, actual business use case and what kinds of results you are interested in.

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