Your Options for Big Data Solutions and What They Can Bring to Your Business

Every growing organization has to deal with the problem of data flowing in at an exponentially increasing rate from a large number of data sources. However, the amount of time available to do something meaningful with that data is reducing. Businesses must develop big data solutions for fast and rewarding business outcomes. 

Big data is a complex information system and cannot be efficiently handled using traditional data processing methods. When handled right, big data can help companies resolve their core problem areas and even bolster their security. To achieve this, they must have a meaningful data and analytics strategy.

Big data is beneficial for many businesses because it helps them in the following ways:

  • Detect the reasons behind problems quickly.
  • Instantly create problem-specific solutions.
  • Recalculate the whole risk portfolio.
  • Proactively identify malicious cyber activity before it can unleash its worst consequences.

The most prominent and popular big data solutions in demand in 2022 are:

The Hadoop Ecosystem

Apache Hadoop is one of the most dominating big data solutions available. It is used for distributed processing of large datasets. Many large enterprises are using Hadoop and related technology for big data analytics. 

Spark

Spark forms part of the Hadoop ecosystem and is another big data solution that’s rapidly scaling the popularity charts. It can process big data within Hadoop. Importantly, it works a hundred times faster than a standard Hadoop engine. 

Data Lakes

Leading enterprises are setting up data lakes to access huge databases more effortlessly. 

These are huge data warehouses. They gather data from diverse sources and store it in its natural state. However, data lakes are different from data warehouses. It also collects data from different sources. But it is processed and structured for secure storage. 

Data lakes can be the best big data solution for enterprises looking to store data but are not yet sure of its usage. 

NoSQL Databases

Traditional database systems are designed to store information in a structured format. Database administrators manage the data with the help of SQL, a special language.

NoSQL databases are explicitly used for storing unstructured data and ensuring fast performance. With the growing big data trend and applications, the demand for NoSQL databases has become increasingly popular. 

Predictive Analytics

Predictive analytics is a sub-set of big data analytics. The platform is designed to leverage historical data and behavior to forecast future events. It depends on techniques like data mining, modeling, and machine learning to predict the future course of data. The big data solution is generally used for fraud detection and comprehensive business analysis.

In recent years, the rapid progress in artificial intelligence has powered huge improvements in predictive analytics solutions capabilities. Consequently, enterprises are investing more in big data solutions with predictive capabilities. Today, many vendors are offering this big data solution. 

Data Governance Solutions

The concept of data governance is closely associated with data security. Data governance is a broad term that considers all the processes specific to usability, integrity, and data availability. So, it provides the foundation to ensure that the data used for big data is accurate and appropriate. It also provides an audit trail. This helps analysts or data managers understand the origin of data.

Self-Service Capabilities

Effective and efficient handling of big data entails using the expertise of big data scientists and experts. However, not all organizations can employ the best because they are in short supply. Also, for some organizations, the fat salaries that data scientists command can be a deterrent. This has led to organizations increasingly depending on self-service capabilities. 

Business Intelligence and analytics platforms are available to help businesses meet big data management issues. These big data solutions help organizations meet the organizational requirements for greater accessibility, deeper analytical insights, and agility. They also help organizations shift from IT-led systems to business-led agile analytics. 

With the trend for using self-service capabilities increasing, organizations are dependent on top vendors who offer self-serving capabilities. They help organizations achieve their big data initiatives and add value to their business.

Artificial Intelligence

Artificial Intelligence (AI) technology has become an indispensable element of nearly every business nowadays. It is not off the mark to state that the big data trend is the key reason for driving advances in AI. This is specifically true in two subsets of the AI discipline. They are Machine Learning and Deep Learning. 

In big data analytics, machine learning technology helps systems take a deeper look at historical data. ML can recognize patterns and predict future outcomes. Indeed, deep learning relies more on artificial neural networks. It makes use of multiple layers of algorithms to evaluate data. 

Streaming Analytics

Businesses are gaining confidence in handling big data analytics thanks to a large number of tools available. They now want faster access to insights. Streaming analytics is a technology with the ability to analyze data accurately. 

This big data solution can accept data input from numerous and disparate sources and process it quickly. Moreover, streaming analytics can deliver insights immediately. This is particularly necessary for new IoT deployments.

With demand growing for streaming analytics, many business vendors offer products with advanced streaming analytics capabilities. Companies looking to incorporate this big data solution can have a broader range of options to choose from.

Edge Computing

Edge Computing is one of the most sophisticated big data solutions available to organizations today. It acts differently than cloud computing in many ways. Edge computing systems analyze data very closely at its source. Unlike traditional methods, it doesn’t transmit data to a centralized server. 

A key advantage of using the edge computing system is that it decreases the volume of information transmitted over the network. So, this helps reduce network traffic and the costs associated with it. It also eases the demands on data centers. Edge computing helps free up capacity for other workloads. It also eliminates a potential single point of failure.

Conclusion

Becoming a data-driven organization involves a disruptive shift. You might not be ready for many things associated with such a big move. Developing and executing a big data strategy takes a lot of planning and effort. Make sure you understand the processes before you make the right choice. We highly recommend listing the services of a big data professional.

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