What Is Big Data Architecture?

Big data architecture is the overarching system used to ingest and process enormous amounts of data (often referred to as "big data") so that it can be analyzed for business purposes. The architecture can be considered the blueprint for a big data solution based on the business needs of an organization. Big data architecture is designed to handle the following types of work:

• Batch processing of big data sources.
• Real-time processing of big data.
• Predictive analytics and machine learning.

A well-designed big data architecture can save your company money and help you predict future trends so you can make good business decisions.

Benefits of Big Data Architecture

The volume of data that is available for analysis grows daily. And, there are more streaming sources than ever, including the data available from traffic sensors, health sensors, transaction logs, and activity logs. But having the data is only half the battle. You also need to be able to make sense of the data and use it in time to impact critical decisions. Using a big data architecture can help your business save money and make critical decisions, including:

• Reducing costs. Big data technologies such as Hadoop and cloud-based analytics can significantly reduce costs when it comes to storing large amounts of data.
• Making faster, better decisions. Using the streaming component of big data architecture, you can make decisions in real-time.
• Predicting future needs and creating new products. Big data can help you to gauge customer needs and predict future trends using analytics.

Challenges of Big Data Architecture

When done right, a big data architecture can save your company money and help predict important trends, but it is not without its challenges. Be aware of the following issues when working with big data.

Data Quality

Anytime you are working with diverse data sources, data quality is a challenge. This means that you'll need to do work to ensure that the data formats match and that you don't have duplicate data or are missing data that would make your analysis unreliable. You'll need to analyze and prepare your data before you can bring it together with other data for analysis.

Scaling

The value of big data is in its volume. However, this can also become a significant issue. If you have not designed your architecture to scale up, you can quickly run into problems. First, the costs of supporting the infrastructure can mount if you don't plan for them. This can be a burden on your budget. And second, if you don't plan for scaling, your performance can degrade significantly. Both issues should be addressed in the planning phases of building your big data architecture.

Security

While big data can give you great insights into your data, it's challenging to protect that data. Fraudsters and hackers can be very interested in your data, and they may try to either add their own fake data or skim your data for sensitive information. A cybercriminal can fabricate data and introduce it to your data lake. For example, suppose you track website clicks to discover anomalous patterns in traffic and find criminal activity on your site. A cybercriminal can penetrate your system, adding noise to the data so that it is impossible to find the criminal activity. Conversely, there is a huge volume of sensitive information to be found in your big data, and a cybercriminal could mine your data for that information if you don't secure the perimeters, encrypt your data, and work to anonymize the data to remove sensitive information.

What Does Big Data Architecture Look Like?

Big data architecture varies based on a company's infrastructure and needs, but it usually contains the following components:

• Data sources. All big data architecture starts with your sources. This can include data from databases, data from real-time sources (such as IoT devices), and static files generated from applications, such as Windows logs.
• Real-time message ingestion. If there are real-time sources, you'll need to build a mechanism into your architecture to ingest that data.
• Data store. You'll need storage for the data that will be processed via big data architecture. Often, data will be stored in a data lake, which is a large unstructured database that scales easily.
• A combination of batch processing and real-time processing. You will need to handle both real-time data and static data, so a combination of batch and real-time processing should be built into your big data architecture. This is because the large volume of data processed can be handled efficiently using batch processing, while real-time data needs to be processed immediately to bring value. Batch processing involves long-running jobs to filter, aggregate, and prepare the data for analysis.
• Analytical data store. After you prepare the data for analysis, you need to bring it together in one place so you can perform analysis on the entire data set. The importance of the analytical data store is that all your data is in one place so your analysis can be comprehensive, and it is optimized for analysis rather than transactions. This might take the form of a cloud-based data warehouse or a relational database, depending on your needs.
• Analysis or reporting tools. After ingesting and processing various data sources, you'll need to include a tool to analyze the data. Frequently, you'll use a BI (Business Intelligence) tool to do this work, and it may require a data scientist to explore the data.
• Automation. Moving the data through these various systems requires orchestration usually in some form of automation. Ingesting and transforming the data, moving it in batches and stream processes, loading it to an analytical data store, and finally deriving insights must be in a repeatable workflow so that you can continually gain insights from your big data.