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Showing posts from March, 2019

Apache Kafka

Data streaming Streaming data is data that is continuously generated by different sources. Such data should be processed incrementally using Stream Processing techniques without having access to all of the data. In addition, it should be considered that concept drift may happen in the data which means that the properties of the stream may change over time. It is usually used in the context of big data in which it is generated by many different sources at high speed. Data streaming can also be explained as a technology used to deliver content to devices over the internet, and it allows users to access the content immediately, rather than having to wait for it to be downloaded. Difficulty in data streaming   Mainly we can see two problems when talking about data streaming. ·         How to get data from many different sources flowing into your cluster ·         How to process it when it is there ...

Redis Clustering

Redis Clustering Overview Re mote Dictionary Server (ReDis) is an in-memory, key-value database, open-source, networked, commonly referred to as a data structure server written in ANSI C. The key feature of Redis is, it can store multiple types of data as key-value pairs. According to the monthly ranking by DB-Engines.com, Redis is the most popular key-value store. Redis’s ability to store and manipulate high-level data types. These data types are fundamental data structures (lists, maps, sets, and sorted sets) that most developers are familiar with.  Redis’s exceptional performance, simplicity, and atomic manipulation of data structures lend itself to solving problems that are difficult or perform poorly when implemented with traditional relational databases. I would like to discuss Redis in ·            Setup Redis clusters in local machine ·       ...