As we all know, today's computerised world has made working for any company or business easier. Big data analytics is one aspect of computer technology that helps develop a data pipeline that is easy to access and work with. It comprises various big data architecture layers that provide a means to organise the architecture components.

On the other hand, if we talk about Big Data Technology Stack, it helps in ensuring the smooth functioning of the entire stack of technologies. Here is a guide for you on the layers of big data architecture and the big data technology stack.

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Introduction to Big Data Architecture

Big data architecture is the backbone of data analytics. It is designed to ingest, process, and analyse data optimally. With the help of big data analysis tools, we can abstract important information from uncertain data and work on making valid, well-planned decisions. The working of big data is carried out with the help of various layers: Consumption, Assimilation, Processing, Enquiry, Storage, and Visualisation.

Below is the detailing of these layers with their function.

Different big data architecture layers

Consumption Layer

It is the first step toward the journey of the data collected from various sources.  Data consumption is the method of privatising and dividing the data to enable it to flow effectively and easily from the layers of the Data consumption process flow.

Assimilation Layer

Data is transported from the consumption layer to other data pipelines for analysis during this layer. The disintegration of data components occurs during this layer.

Processing Layer

It is the layer in which the previously assimilated data is processed to give them the different destinations and classify the data flow. This layer of Big Data Architecture specialises in the data pipeline processing system.

Enquiry Layer

The analytic processing of data occurs in this layer. This layer assists the next layer by collecting data from sources. Thus, it helps in faster moments of the data layers.

Storage Layer

Data Storage Layer is very useful in deciding where to store the large data efficiently in the case of acclimatisation of large data in your system. We can say that whenever there is an overload of data in your system, the storage layer helps you manage the data, ultimately preventing your system from collapsing.

Visualisation Layer

This layer is the most prestigious tier in big data architecture. The architecture of data has this layer to seek the attention of people and make the process of searching more understandable. It is the layer where the data pipeline users recognize the value of data.

What is a big data technology stack?

Big Data Technology Stack generally comprises numerous layers: data layers, data ingestion and integration layer, data processing layer, and data analytics and BI layer. Each of these layers is dependent on one another and plays a vital and distinct role in enabling the smooth functioning of the entire big data technology stack.

Explanation of big data technology stack layers

big data stack

The data layer

The technology at the ground level works to store the excess of new data coming from old sources like OLTP and the new and slightly structured places like log files, different sensors, website analytics, various documents, and media stores. The storage in this layer takes place inside the cloud or on the local and virtual resources. Data Storage Systems like Amazon S3, Hadoop HDFS, and MongoDB are some of the systems available under this layer. The organisation uses them on a large scale to store the data efficiently and in an organised manner.

The data ingestion layer

If the aim is to make a large data store, importing the data from their source of origin into the data layer is essential. These specialised tools include data warehouses. We need the data pipeline for the ingestion of the data. One can take the support of a superior natural system of big data integration tools, which includes powerful tools that extract the data from various original locations, modify it, and fill it into a destined system of one's choice. Examples of tools used to ingest big data are Stitch, Blendo, Apache Kafka, etc.

The data warehousing layer

The data warehousing layer optimises the data to enable easy and smooth analysis. Also, an engine acts as a competitor and helps run the queries. This layer breaks down the numbers to allow us the process of analysis. Analysts and data scientists use it to run SQL enquiries on massive volumes of data. Some big data also needs excess computing ability to run SQL queries. If you want to process data at optimum scale, then data warehouse tools are the best options, while if you want to store a large amount of raw data to accommodate several use cases, then go for the data lake. If you want to process and analyse the data stored in the data lake, you will require other technologies to assist the data lake.

Some of the most leveraged robust cloud data warehouses are Apache Spark, Amazon Redshift, SQream, PostgreSQL, etc.

Data analytics layer

After the data is collected for analysis by the data layer, it is mixed together by the integration layer. At the same time, it is optimised and organised, and the queries are run against them by the data processing layer. Finally, the data analytics and BI layer are needed to make appropriate decisions based on the data. By working with this layer, you can run the inquiries to provide the solutions for the questions the business asks, examine the data from different viewpoints, develop dashboards and make advanced and beautiful visualisations.

The users can use BI tools such as Looker, Tableau, Power BI, etc., to further increase their business components of the data analytics to the next level.

Wrapping up

The rapid and unpredictable growth and inventions in data for the files or apps, AI, and machine learning have made it essential for us to find new constructive ways to integrate, analyse and store considerable quantities of data produced daily. Apart from this, the analysis reports must be decision-driven. The layers of big data architecture and Big Data Technology Stack have made this process possible and more manageable. These systems save time and make your data organised and easy to store.

Working with these systems is comfortable once you learn their basics. If you want to learn on an advanced level, take up a Big Data course on our platform, and learn from industry experts.