The continuous growth of data has led to large corporations investing heavily in technologies around large data volumes, allowing them to gain useful business intelligence that was unavailable to their smaller competitors. The evolution of public clouds has made big data technologies accessible to small businesses and startups. By using new advances in Data Architecture, they can now reap the same benefits as their larger competitors, gaining insights and supporting intelligent decision-making. The primary advantage for startups and small businesses is their ability to implement these insights much more quickly and efficiently.
Early efforts were generally located on premises, with large organizations collecting, organizing, and analyzing massive amounts of data. However, since then, public cloud platform vendors have provided an environment that supports massive data volumes, easily and inexpensively. Cloud users can now develop Hadoop continuous intelligence, explainable artificial intelligence, and augmented analytics clusters within the cloud, run them as long as needed, and then shut down the project, with the understanding they will only be charged for the time used.
Large data sets can be analyzed for patterns, trends, and associations, particularly those dealing with human behavior and interactions. Small businesses have the strength of being much more flexible and adaptable than their larger competitors, and this can be used to the organization’s advantage. Insights gained from big data analytics “should” be used to alter and reorganize a business, providing solutions for problems that have not yet been exposed. Big Data Architecture lays the framework for working with large data projects.
Improved Data Architecture
Data Architecture has come a long way. The term Big Data Architecture is often used to describe a complex, large-scale system that gathers and processes massive data volumes for analysis, with the results used for business purposes. These types of architectural systems include a scalable storage system, an automation process, and tools for researching the data.
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. People must be able to make sense of the data and use it in time to impact critical decisions. Using advanced Data Architecture can help your business save money and make critical decisions, by:
- Reducing Costs: Hadoop (which is open-sourced/free) and cloud-based analytics will significantly reduce the costs of storing massive amounts of data.
- Creating New Products: Helps to gauge customer needs.
- Faster and Better Decisions: The streaming aspect of advanced Data Architecture supports decisions in real-time.
To function well, big data analytics requires a functional architecture to get the best outcomes. The architecture is the foundation supporting big data analytics. Big Data Architecture is designed to handle the following types of work:
- Predictive analytics
- Batch processing
- Real-time processing
- Machine learning
- Predicting future trends for business intelligence purposes
The Components of Advanced Data Architecture
Discovering business intelligence in large data volumes can be a difficult task. Advanced analytics is a complex process requiring a number components that govern the gathering of data from multiple sources, and synchronization between these components is necessary for optimizing their performance. Advanced architectural styles vary, depending on an organization’s infrastructure and needs. However, they typically contain the following components:
- Data Sources: Sources can include data from real-time sources (for example, IoT devices), data from “other” databases, and files generated from applications.
- Real-time Message Ingestion: This deals with streams of data captured in real-time, and then processed with minimal down-time. Many real-time processing solutions require “message ingestion storage” to serve as a buffer, and to assist with reliable delivery, scale-out processing, and message queuing.
- Data Storage: Storage is needed for the data that will be processed via the architecture. Often, data will be stored in a data lake, which is a large unstructured database that scales easily.
- Batch processing and Real-time Processing: The ability to handle both static data and real-time data. This is necessary because large volumes of data can be processed efficiently with batch processing, and real-time data can be processed immediately. Batch processing deals with long-running projects that filter, assemble, and organize the data for analysis.
- Analytical Data Store: A separate storage space for data after it has been prepared for analysis. All prepped data is stored in one place so the analysis can be comprehensive and accomplished efficiently. (Storage in the cloud.)
- Analysis or Reporting Tools: After gathering and processing data from various sources, tools are needed to analyze the data. Often, business intelligence tools are used to do this work, but a data scientist or a big data analyst might be needed to explore the data.
- Automation: Data moving through the various systems will require orchestration, typically in the form of automation.
Advanced Architecture and Big Data Analytics
Advanced Data Architecture often incorporates Hadoop data lakes, using them as the primary data store for streams of raw, incoming data. With this type of architecture, data can be directly analyzed within a Hadoop cluster, or be run through an engine like Spark. Dependable Data Management is an essential first step in the process of big data analytics. The data can be analyzed using software designed for advanced analytics processes. This includes using tools for:
- Data mining
- Predictive analytics
- Machine learning
- Deep learning
- Text mining
Challenges in Working with Advanced Architecture
Working with diverse data sources makes maintaining Data Quality a challenge. It is important to ensure data formats match, and to avoid having duplicated data, or missing data, which would make the analysis unreliable. The data should be screened and prepared before combining it with other data used in the analysis.
The accuracy of big data comes from its volume, and the statistics used in finding patterns. However, volume can quickly become a significant problem. If the architecture has not been designed to scale up, problems can develop, very quickly. First, the price of supporting the infrastructure can increase if scalability is not planned for. Second, if scaling is not available, performance can drop significantly. This is where the cloud really comes in handy. Both issues can be addressed without massive expenditures on your on-premise system.
While big data can provide great insights into a customer base, it can be challenging to protect that same data from hackers. Security, as part of the architecture, should be addressed when working with large data volumes. Hackers might try adding their own data or mining the data for sensitive or embarrassing information. A cybercriminal can create false data and deposit it into a data lake. Additionally, there can be a massive amount of sensitive information in such data, and this can be mined if perimeters are not secured. Encrypting the data and removing sensitive info can help.
Finding skilled labor can be difficult. Many advanced technologies require highly specialized skills, and use languages and frameworks that are not used in general application architectures. A data scientist is quite expensive, and can be difficult to find. A big data analyst may be just as hard to find, but should be less expensive. (And consider hiring them on a freelance basis. While scheduling will be a bit more difficult, you won’t have to pay for full time, long-term labor and benefits.)
Data Architecture and the Cloud
Ideally, a new organization will create a hybrid cloud, using both an on-premise cloud and one or more public clouds. It may be initially less expensive to start with public clouds and add an on-premise cloud when the cash flow is available. A hybrid cloud philosophy provides startups and small businesses with the ability to achieve a competitive advantage. A hybrid cloud involves working with various public clouds and an on-premise private cloud. Cost reductions are typically the primary goal, but businesses also gain benefits from the flexible IT infrastructure and the scalability benefits.
The tools that are available for working in public and private clouds have the potential to maximize efficiency when working on digital projects. Spreading the workloads between private and public clouds gives a business greater flexibility and provides more ways to use the data.
Hadoop is an open-sourced (free) and popular software framework designed to process large data volumes and is a common component in Data Architecture. However, Hadoop can be difficult to install, configure, and support. Various public clouds, such as Google Cloud, Microsoft Azure, or Amazon AWS can simplify access to a Hadoop system, and can seriously reduce the time, cost, and difficulties associated with implementing Hadoop in an on-premise system.
Most major cloud providers typically offer a few free months of service, allowing newcomers to learn the system. Additionally, public cloud providers offer streaming and messaging, machine learning platforms, data warehouses, and productivity tools. Overall, these public cloud benefits have prompted many businesses to pursue cloud adoption.
The Future of Data Architecture
Continuous intelligence, explainable artificial intelligence, and augmented analytics are currently hot topics in Data Architecture. Continuous intelligence and explainable artificial intelligence both use AI, while augmented analytics uses machine learning. These tools increase speed, productivity, and understanding.
The ability to analyze semi-structured and unstructured data is going to improve significantly in the next few years. Video, text, and other modes of media will need new forms of architecture, and new technologies to analyze these medias. For instance, many marketing departments are searching for ways to research sentiment and brand issues using postings on Twitter, Facebook, and YouTube.
Image used under license from Shutterstock.com