The word big data is undoubtedly meaningful. But if the literal meaning is considered, the outcome will be as appropriate as the literal one. Big data means a certain volume of data regardless of the genre. A volume that is impossible to handle by usual means, without automating the process to a certain extent. Thus handling this data requires a specific set of abilities. And based on the data requirements this ability is subject to upgrade by quantitative means. This infrastructure is better known as big data architecture. The data an organization handles can be termed big data if the volume threshold crosses a certain point. Deep sing on the scale of operations and the requirements of a venture. The amount of data needed can range from a few hundred gigabytes to thousands or terabytes. And the processing of the same is diverse and requires unique attention. Big data architectures must consist of all the specialized amenities that are needed for making successful use of big data.
The genre of task
Big data architectures are deployed under certain circumstances, where the volume makes it impossible to handle by traditional means. Such as,
- Humongous amounts of data storage, that are impossible to store by conventional storage paradigms.
- Transform the same volumes into workable structured data forms.
- Analyze, process, and perform descriptive, predictive, and prescriptive analytics. That too, in real-time if needed.
Components of a big data architecture
A data source
Ethically accessible Data sources in our times are available in abundance.
- The source can be static files that are produced by services and applications as logs.
- Real-time data is generated by an active entity like the popular devices that form the internet of things.
- Relational databases are another prominent source. The size is huge and due to ages of data accumulation
The storage module
The storage of big data is not a conventional process. The size makes the storage a challenging affair. And organizing the same is also difficult. The storage server for big data is termed a data lake. Like the azure data lake. These lakes can hold on to gargantuan amounts of data in a variety of formats.
Batch processing
Batch processing is done for huge amounts of similar data. The preprocessing steps like filtering, structuring, and formatting are done with entire batches of similar data in terms of utility and origin. These tasks can be done through u-SQL in an azure data lake, in HDinsight Hadoop cluster the same is performed by Pig, hive, or custom maps. In HDinsight spark clusters, python and Java are used.
Real-time message ingestion
Real-time message reception systems are powered by entities like azure event hubs, azure IoT hubs, and Kafka. These message reception paradigms are deployed when the data sources are dynamic and generate real-time data, like the devices involved in IoT. The paradigm is used with message buffering systems and real-time analytics capabilities. However, the real-time message ingestion systems are like simple stores but can be used as a buffer for messages.
Processing of data streams
Real-time message ingestion is a prerequisite for stream processing. The stream data is generated and must be utilized in real-time. After the capture of real-time messages, the process involves formatting, aggregating, and arranging data, in a nutshell preparing the data for analysis. and then the data is written in an output sink. Azure stream processing services are a good stream management service that runs SQL queries.
Analytical stores
Analytical stores are specialized and designed for specific analytical needs. Data formatted in a specific manner is stored in an analytical store for quick and easy access. In the case of most standard business intelligence solutions, an analytical store is used as a Kimball-style relational data warehouse. An alternative paradigm of representation is through low latency NoSQL technology like Hbase or interactive hive databases.
Analysis and reporting
A big data architecture empowers users through a data modeling layer, incorporated into the architecture. Something like a multidimensional OLAP cube or tubular data model in azure analysis services. Big data analytics and reporting can be transformed into an interactive exploration. And services like Azure make things possible by supporting analytical notebooks like Jupyter.