A Look at Big Data Architecture and its Components, Limitations and Advantages

As you might imagine, big data architecture is an overarching infrastructure that allows for the analysis of large data sets. It allows for the processing, storing, and analyzing of large data sets. Big data architecture is a combination of complex components that have been developed to help organizations manage their data. These components include:

• Data sources
• Processing tools
• Data Analytics tools
• Data visualization tools

Big data architecture consists of these four things, as well as other solutions and processes. However, all big data architectures consist of some variation on these four components.

Components of big data architecture

Every Big Data Architecture is different, depending on what each business needs to accomplish with its data. However, regardless of who's using the technology and how they're using it, every big data architecture has a few common components:

1. Data ingestion

Before you can use big data to gain actionable insights from your data, you'll need a way to get that data into the system. Data ingestion is a key component of data infrastructure, enabling organizations to derive business value from data. Ingestion usually involves collecting raw, unstructured information from various sources and then consolidating it into a single repository for processing as needed. In other words, if you want to collect and analyze customer purchase history along with social media comments about your brand, you'll probably have to gather that info from two different places before making sense of it altogether.

Why do we need Data Ingestion?

Data is becoming more valuable than ever before and companies are looking to leverage data to its full potential by analyzing it to gain actionable insights. However, since data comes from various sources and formats, you need efficient tools and technologies to ingest it into a central platform so that it can be processed, analyzed, and stored effectively.

If the data is ingested using an inefficient method or tool, it could delay the process of processing the data and deriving insights from it. It could also lead to corrupting the data while it is being ingested into the system due to incorrect formatting or other technical issues. With many emerging use cases for Big Data analytics and Machine Learning (ML), organizations are keen on adopting new tools and technologies for data ingestion.

1. Data Ingestion Tools
2. Flume
3. Kafka
4. Sqoop
5. Nifi
6. Embulk
7. Lumberjack/Logstash
8. Forwarder (LWF) aka beats Apache Storm and Trident

What is a Data Lake

A data lake is a way of storing data. While traditional databases are great for structured data, sometimes it's better to store unstructured data in a storage repository where you can see the relationship between all its parts. A data lake is ideal for this as it allows us to dump in all the data we want without structure and then apply that structure when we need it.

Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business query is executed on the data, the system uses the metadata to retrieve only the relevant data, rather than processing all of the structured tables in a relational database.

Data lakes can hold raw copies of source system data and transformed versions of source system data used for tasks such as reporting and analytics. If you have multiple groups running similar queries against similar subsets of tables, each group can build structures optimized for their own needs without impacting other users of the warehouse.

Data lakes are most suitable when:

• To analyze both structured transactional data and unstructured machine-generated (social media) or human-generated (survey responses) content

• The cost associated with preparing, transforming, and loading this type of content into an existing enterprise warehouse outweighs any business benefit you would gain from being able to query it

• To be able to easily combine different types of content without needing to modify the overall enterprise architecture

  
  

2. Staging Layer/Storage

Once that information has been ingested, it needs somewhere to live while waiting to be analyzed. In the old days (by which I mean pre-cell phones), this meant building giant warehouses full of servers and hard drives sitting on shelves or stacked up in rows like library books—hence the term "data warehouse." More recently (and less expensively) businesses have begun storing their big data on cloud-based platforms offered by providers like Amazon Web Services (AWS) or Microsoft Azure.

These services make it easier than ever for big data analytics companies of any size to access scalable infrastructure without having huge capital expenditures related to buying equipment outright or maintaining an in-house IT team.

The Data Warehouse stores cleansed, historical data that is used for analytics. This is where the cleansed data will be sent all after it passes through the Staging Layer.

3. Processing layer

The processing layer is where you turn all your raw data into something meaningful by parsing through individual records one at a time until only relevant pieces remain (and then analyzing those pieces).

This kind of analysis is done by writing custom algorithms or working with third-party software designed specifically for handling large amounts of incoming information quickly enough so users don't notice any slowdown when accessing reports generated from past queries; either way though there are always going to be some limitations due to limitations inherent within computers themselves since they aren't perfect machines capable at 100%.

4. Presentation/Visualization layer

The presentation layer is the most critical component of any big data architecture. It's where insights are presented to decision-makers in a way that makes it easy for them to understand the data and take action on it. This is done through reports and dashboards that can be accessed from a web browser or mobile phone.

Types of architectures for big data

There are five main types of big data architectures.

• The batch architecture is a traditional way of processing data sequentially.
• In a streaming architecture, you're processing data in near-real-time.
• Lambda and kappa architectures are both ways to process streaming data—the key difference between the two is that lambda architecture includes batch processes of the same stream, while kappa architecture assumes that only the most recent version of its stream is relevant for analysis.
• A hybrid architecture combines batch and streaming methods for processing big data.

How to Build a Big Data Architecture

Building a big data architecture sounds like an overwhelming proposition, but it can be broken down into pieces. Here's how to design a big data architecture that meets the enterprise's needs.

• Define the goals and objectives: Ask yourself what you want to achieve with big data analytics, and what information is needed to get there.
• Identify the relevant stakeholders: Decide who will use the analytics and what they need to accomplish their goals.
• Determine which technologies are required: You should have a good understanding of all the associated technologies, such as machine learning, streaming data processing, and real-time analytics before you begin building your big data architecture.
• Develop a robust security plan: This includes everything from network security and user access control to encryption of sensitive data during transit and at rest.
• Create a proof of concept: A POC is a valuable tool for ensuring the viability of your big data architecture before committing any resources to its deployment.

Limitations of a Big Data Architecture

The limitations of big data platforms often include:

• Accuracy: Some sources of big data contain erroneous or inaccurate information. In the real world, there are many reasons why data is incorrect, but some examples are human error and system errors when capturing the data. This can make it difficult to ascertain whether you can rely on a given source of information.
• Trustworthiness: Not all sources of big data are trustworthy. For example, some organizations will use social media sentiment analysis to help determine customer satisfaction with their products and services, but there's no way to know whether these messages were written by real customers or mock accounts run by competitors trying to spread misinformation. If this method is used as part of the analytics strategy then you must verify the authenticity before making any decisions based on those results!
• Timeliness: Big Data is not always timely or accessible promptly which can make it hard for businesses who need immediate insights from their analytics platforms. For example, if an organization needs insights about what customers think about their new product launch but does not have access to social media sentiment analysis tools yet then they might not be able to get timely feedback on customer satisfaction levels until too late into production processes - potentially resulting in wasted resources due to lack of foresight."

Advantages of a Big Data Architecture

One of the major advantages of a Big Data architecture is the ability to process and store large amounts of data. By definition, Big Data refers to extremely large data sets that may be unstructured, semi-structured, or structured. Such data sets are so voluminous and complex that they are impractical to manage with traditional database management tools.

Big Data architectures are particularly useful in the fields of science, engineering, medicine, and business analytics. For example, in science and engineering, the company might have millions of images from a satellite or robotic vehicle that must be processed for specific anomalies or characteristics. In medicine, they also might perform genetic tests on many thousands of patients to determine which genes are associated with a specific disease. In business analytics, you might analyze social media feeds from millions of users to determine their attitudes toward your brand or business.

In general, Big Data architectures use multiple technologies working in parallel to ingest, store and process huge amounts of data as quickly as possible.

What Makes a Good Big Data Architect?

An architect must possess certain qualities to succeed.

• An understanding of the business: A solid understanding of the business you're working in is essential. You need to know how different functions impact each other. As an architect, often will be called upon to make trade-offs between competing priorities and interests when creating the architecture for a solution.
• Communication skills: Being able to communicate with both technical and non-technical people is key. A lot of interaction will happen will be interacting with technical staff such as developers, software engineers, and DevOps consulting companies. But you will also need to speak frequently with non-technical personnel—including executives who may not have a strong technical background—to influence your organization's overall technology strategy and direction.
• People management skills: As an architect, you are often leading teams and/or working with external vendors on project implementations; thus, it is critical that you can work well with others

How does Data Quality Affect Big Data Architecture?

Data quality is a crucial element in big data architecture. It is equally important to consider data quality while working with any other database, but the volume and variety of data present in big data make it even more important.

Data quality assessment helps in determining if the required data is present within the system or not and if that data meets specifications. Data cleansing tools are used to improve the quality of existing data by removing unnecessary or inaccurate information. These tools along with other techniques also help in improving the overall efficiency of systems.

How does Big Data Analytics Tools Affect Big Data Architecture?

With the rising popularity of big data analytics tools and data warehouses, it is essential to understand the role they play in big data architecture.

Big data analytics tools help improve data quality. When using big data tools, it can see in real-time what the customers are doing on your website and make the necessary changes to enhance their experience. With such insights, in addition, it can easily keep track of what needs to be done as a business to improve its marketing efforts.

If a customer were to browse for products on your website, but not make any purchases due to issues with image loading or poor product descriptions — you would be able to catch this problem (and many others) through specific dashboards that provide valuable insights into user behavior.

Big data analytics tools help collect new types of information that most traditional business intelligence tools cannot process effectively because they don't have direct access points with different sources of outside information (e.g. social media). With social media platforms being one source where consumers spend the most time interacting (liking content & sharing ideas), big companies need these analytics solutions more than ever before if they want their marketing campaigns successful and profitable!

How does Big Data Visualization Tools Affect Big Data Architecture?

Data visualization is one of the best ways to communicate insights and for decision-making. The data architect, who is responsible for designing the structure of the big data and its internal network, must also be skilled in data visualization. Data visualization allows the architect to build a comprehensive model of the system and helps identify areas where certain processes can be implemented. The data architect can use this information to implement processes and procedures that will enable them to complete their tasks more efficiently and with less effort.

Additionally, data visualization helps the architect analyze what is happening in the business daily. This can then allow them to make better decisions about which processes are most important, what information should be reported on, and which processes need improvement. For example, if an architect knows that they have limited bandwidth because they are running several reports at once, they can use visualizations to determine which reports take up more bandwidth than others. If this information is not available through other means, then it will not be possible for them to make effective use of their resources and achieve their goals.

Challenges with Big Data Architecture and Their Solutions

When planning the big data architecture, it's important to address issues that could arise and how to solve them.

Defining the sources and making sure they're high quality is critical to this process. Inadequate data can cost a business a lot of money. An article by McKinsey defines three ways poor data quality causes economic losses: inaccurate decision-making, the poor performance of a system/app, and late or no payments on accounts receivable.

This is why getting a good understanding of where the data comes from is an essential part of developing a useful big data set. Data analysts and data architects need to work closely together for this process to be successful—a good analyst will have the technical skills needed to create pipelines from multiple raw sources, whereas an architect will understand the larger goals behind the creation of these pipelines.

A robust architecture will ensure that once these sources are compiled into one place, there is enough material for it to be useful on its terms (versus being thrown out). The third step in this process involves working with data scientists who can interpret it for use in predictive analytics and machine learning applications.

Conclusion

To conclude, adopting big data architecture is a relatively simple process with huge benefits.

First, identify the problem areas and what is hoped to be accomplished by implementing big data architecture.

Then, examine the solutions that are possible within the situation and select the one that will best meet the project's needs.

Take note of any potential obstacles in the way and plan for how to overcome them; this could include anything from making sure to have enough server space or acquiring approval from management.

Finally, move forward with a clear vision of how to implement the solution and where it will take you in the long term!

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