The concept of Big Data in the field of data management and analytics has encompassed several new milestones and technology developments over the years. Many Big Data techniques have evolved from the realms of hype to critical differentiators that are being given due attention in this age of the digital world.
Apache Spark is part of the Hadoop ecosystem, but its use has become so widespread that it deserves a category of its own. It is an engine for processing big data within Hadoop, and it’s up to one hundred times faster than the standard Hadoop engine, MapReduce.
In the Big Data Maturity Survey, 25 percent of respondents said that they had already deployed Spark in production, and 33 percent more had Spark projects in development. Clearly, interest in the technology is sizable and growing, and many vendors with Hadoop offerings also offer Spark-based products.
The unique feature of a blockchain database is that once data has been written, it cannot be deleted or changed after the fact. In addition, it is highly secure, which makes it an excellent choice for big data applications in sensitive industries like banking, insurance, healthcare, retail, and others.
Blockchain technology is still in its infancy and use cases are still developing. However, several vendors, including IBM, AWS, Microsoft and multiple startups, have rolled out experimental or introductory solutions built on blockchain technology.
Kafka is a must because it is a great glue between various systems right from Spark, NiFi to third-party tools. And streams of data can be handled efficiently and in real time. Kafka is open source, horizontally scalable, is fault tolerant, extremely fast and a safe option.
Being a distributed system, Kafka stores the messages (simple byte arrays and developers store any object in any format) in topics, and the topics themselves are partitioned and replicated across different nodes.
If you have heard of Apache Spark and Apache Hadoop, then you will have heard about Apache Flink as well. Flink is a community-driven open source framework, founded by Professor Volker Markl — Technische University, Germany. Flink meaning “swift” in German is high performing and extremely accurate data streaming.
Cloud Dataflow is a native Google cloud data processing service integrated with a simple programming model for both batch based and streaming data processing tasks.With this tool, you no longer have to worry about operational tasks including performance optimization and resource management. Through its fully managed service, it is possible to dynamically provision the resources to maintain high utilization efficiency while minimizing latency.
Big Data, in itself, is a quite broad and advanced concept. And due to so many advancements in Big Data techniques and analytics, conditions that allow integrating these prototypes into successful businesses are the need of the hour.The big data ecosystem is constantly evolving and new technologies come into existence very frequently, many of them evolving further and further beyond the Hadoop-Spark stacks. These tools can be utilized to ensure seamless work with security and management, sans any hiccups.