Numerous technological advancements and Big Data initiatives have led to the diversification of the data community. The term Big Data is no longer only about the Hadoop technology stack, rather, it encompasses a large set of robust technologies and initiatives that allow organisations to store immense volumes of data, ensure blazing fast performance and extract meaningful and actionable insights by analysing data with ease. Achieving such a high level of data processing performance requires you to build a robust data analytics stack that encompasses a diverse set of technologies.
This article aims at providing you with an in-depth guide and deep knowledge about the Big Data Technology Stack, its architecture and numerous concepts associated with it. It will help you build a robust and efficient Big Data Technology Stack that matches your unique business use cases and needs.
Table of Contents
Introduction to Data Analytics Stack
A stack represents a set of robust components & modular technologies that allow users to develop powerful and enterprise-grade applications. Similarly, a data analytics stack encompasses diverse technologies that let users and businesses build a robust analytics engine to aggregate, integrate, model and transform data from numerous data sources. It consists of various interdependent layers that make up an effective and fully functioning analytics system, with each layer offering a unique level of processing.
In case you want to learn more about the data analytics stack, you can click here to check out our detailed guide that will answer all your queries regarding it.
Understanding the Architecture of Big Data Technology Stack
A typical Big Data Technology Stack consists of numerous layers, namely data analytics, data modelling, data warehousing, and the data pipeline layer. Each of these is interdependent and play a crucial and unique role, ensuring the smooth functioning of the entire stack of technologies.
You can learn more about these layers from the following sections:
Data Analytics Layer
The data analytics layer is one of the most crucial layers of a Big Data Technology Stack that provides users with a smooth and seamless interface to interact with the analytics engine. It houses numerous visualisations, dashboards, business intelligence tools and compelling reports, allowing users to understand the data and draw actionable insights. With a robust data analytics engine in place, users such as the data scientists can build analytical models, allowing them to draw crucial insights about their business, its operations & performance.
Users can further leverage the business intelligence component of the data analytics layer by using BI tools such as Looker, Tableau, Power BI, etc. to create compelling visualisations that allow them to understand the data better and draw crucial insights.
Data Modelling Layer
Big Data Technology Stack’s data modelling layer allows users to structure, organise and choose their desired data for querying by leveraging numerous tools such as SQL, Dataform, etc. Organisations support data modelling by creating an analytical base table (ABT) that allows them to clean and aggregate data from multiple data sources in the form of a flat table. With ABTs in place, data scientists can work on clean, consistent and accurate data, allowing them to make robust and precise data-driven decisions.
Data Warehousing Layer
A data warehouse allows users to consolidate and aggregate data from numerous data sources in a centralised location. With Big Data Technology Stack’s data warehousing layer, businesses can manipulate, transform, model and analyse their data to create compelling visualisations by leveraging various business intelligence tools and data analytics software.
Traditionally, organisations would make use of complex on-premise hardware to carry out these activities, however, with the advent of cloud, now, most organisations leverage robust cloud data warehouses such as Google BigQuery, Amazon Redshift, Snowflake, Microsoft Azure, etc. to combine & analyse their data seamlessly. Data lakes further act as an effective alternative to data warehouses that allow users to store large volumes of raw data to accommodate several use cases.
Data pipeline layer of Big Data Technology Stack serves a crucial purpose of providing a seamless mechanism or channel that allows users to ingest complex data from a source of their choice and replicate it to the desired destination. With a robust data pipeline in place, users can integrate data from a diverse set of data sources such as databases, applications, files, etc. and then leverage the data pipeline to transfer this data to a data warehouse or data lake with ease.
Understanding ETL and ELT Processes
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the two major processes that allow enterprises to fetch data from a data source of their choice, transform it into an analysis-ready form and then store it in the desired destination.
Most cloud data warehouses support and work seamlessly with the ETL approach by leveraging their ability to carry out complex transformations and deliver analysis-ready data, allowing users to perform analytics effectively.
ELT approach, on the other hand, supports data transformations only after the data is present in the destination.
To build a data pipeline for your unique business use cases, you can either take the manual route by hard coding your data pipeline or take up any pre-built tool. In case you decide to develop your data pipeline manually, you will need to make use of programming languages such as Python, Go, Ruby, Java, etc. to carry out ETL jobs.
Limitations of Building a Big Data Technology Stack Manually
- Building a Big Data Technology Stack from scratch requires you to have a strong technical and fundamental knowledge of programming languages, APIs, frameworks, etc.
- Manually carrying out ETL jobs can be a challenging and time-consuming task that requires you to put in a lot of effort, in terms of developing and maintaining the entire infrastructure.
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Hevo is a consistent and reliable solution for your ETL process. You can enrich your data and transform it into analysis-ready without writing any code. You can also leverage the extensive logging capabilities of Hevo to understand how your pipeline behaves.
Hevo Data, a No-code Data Pipeline, empowers you to ETL your data from a multitude of sources to Databases, Data Warehouses, BI tools, or any other destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.
Hevo supports both pre-load & post-load Data Transformations and, allows you to perform multiple operations like data cleansing, data enrichment, data normalization with just a few clicks. You can either customize these transformations by writing a Python-based script or leverage Hevo’s drag and drop transformation blocks. Learn more about Hevo’s Transformations.
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This article teaches you in-depth about the Big Data Technology Stack & answers all your queries regarding it. It provides a brief introduction of numerous concepts related to it & helps the users understand them better. If you’re looking for an all-in-one solution, that will not only help you transfer data but also transform it into analysis-ready form, then Hevo Data is the right choice for you!
Hevo offers a No-code data pipeline that will take full control of your Data Integration, Migration, and Transformation process. Hevo caters to 100+ data sources (including 40+ free sources) and can directly transfer data to Data Warehouses, Business Intelligence Tools, or any other destination of your choice seamlessly. It will make your life easier and make data mapping hassle-free.
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