Big Data - The Complete Guide
With advancements in technology and the world moving toward digitization, every company is becoming software. With the world generating more data than ever before, modern big data technologies allow organizations to efficiently store, process, and analyze vast amounts of data. From customer behavior and shopping trends, to live traffic data and risk management, big data fuels better business decisions, improves efficiency, and unleashes correlations and predictive analytics. Learn what big data is, how it works, major benefits, and how to get started.
Big Data Explained
What is Big Data?
Big data is essentially a collection of extremely large data sets that cannot be processed by traditional tools, but that can bring a lot of value to business or society. There are three criteria for big data (3 V’s): huge volume, high velocity (constant and fast data generation followed by rapid processing), and great variety (data can come from different sources in a structured or/and unstructured form).
Data can be collected, curated, and analyzed in batch processing mode, or as real-time data streams to derive useful insights for a range of stakeholders.
Why Data is So Important
As data grows exponentially in volume, complexity, at faster throughput, big data becomes crucial for any business to gain insights, improve business operations, mitigate risks, and make impactful business decisions.
By analyzing massive amounts of data, organizations can create new products, estimate the efficiency, understand how to conduct marketing campaigns, optimize their resources, formulate strategy, etc. Data can describe almost everything in business. This means that big data can generate benefits for every aspect of business activity.
Benefits of Big Data
There are two reasons behind the growing popularity of big data: data availability, and accessibility to computing resources.
From improved customer experiences to predictive analytics, big data brings numerous real-life benefits and use cases:
- Building recommendation systems using historical and real-time data from customers' preferences, purchase history, and/or activity
- Sending targeted offers to customers based on their past behavior
- Preventing fraudulent operations in real-time
- Continuously monitoring the success of the new product/division/company, etc.
- Costs reducing by relying on data-driven decisions.
How Big Data Works
The process of working with big data involves data collection, data storing, data analysis, and decision-making. Working with big data also requires the creation of appropriate data pipelines to transfer data between the components of the big data ecosystem. Big data usually consists of the following components:
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Data Ingestion: There are a lot of possible options: web and mobile applications, IoT data, social networks, financial transactions, servers load, business intelligence systems, etc.
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Data Storage Procedures: This component also includes a set of policies regarding data management and data access.
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Data Analysis: This is the component that sees raw data being transformed into valuable insights, actionable business insights, and making data-driven decisions.
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Data Flow: This very important element relates to the form in which data streams from one place to another. The data pipelines should be efficient and reliable enough to cope with the high volume and velocity of big data.
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Big data strategy. This is the broadest component that defines the high-level policies regarding each of the elements mentioned above.
Challenges of Big Data
Due to the sheer volume and complexity of data, businesses often run into roadblocks. Here are the most common challenges that organizations face today when it comes to using big data:
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The continuous growth of data volumes. Regardless of how much more affordable data storage has become in recent years, the continuous growth of data volumes is a persistent problem. Organizations need to be able to process and store every bit of data that is created on a personal and organizational level every day.
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The need for near real-time data processing. Big data is almost always a stream. Even if the aim is to analyze data by batches, the business needs to collect it in a stream format. This means that efficient streaming infrastructure should be in place in order for businesses to be able to process data in near real-time.
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Data security. Nobody except specifically defined users should have access to sensitive data, and setting up the most effective security protocols can be somewhat challenging. Businesses need to find a way to protect transaction logs and data, secure framework calculations and processes, and secure and protect data in real-time, to name but just a few challenges.
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Data integration. Data comes from different sources and in different formats. The challenge for businesses is to integrate the data so that it can be used and analyzed in an acceptable format. Businesses also need to find a solution to merge data that is not similar in source or structure and to do so at a reasonable cost and within a reasonable time.
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Data validation. Before businesses can analyze their data, they need to clean up the existing data and prepare it for further use. Data may be siloed or outdated and validating the data format can be a time-consuming process, even more so if the database that a business uses is large.
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The complexity of data analysis. When using big data, data volumes are massive and high dimensional, which can cause computational challenges. Scalability and storage bottlenecks, noise accumulation, spurious correlation, and measurement errors are all possible challenges that might hinder the process of quickly and effectively analyzing the data.
Real-Time Data Management – The Key to Success
A major challenge in modern data management is the ability to streamline all data types, from all sources and formats into a single pane. The ability to process and integrate real-time streams of data allow for digitalization, speedy time-to-market, quick innovation, and big data analytics at scale.
How Confluent Can Help
Confluent is a data streaming platform designed to integrate data from countless sources at scale, including traditional databases and modern, distributed architectures. Originally envisioned as a fast and scalable distributed messaging queue, it has rapidly expanded into a full-scale streaming platform, capable of not just collecting batches of data, but storage and real-time data aggregation, processing, and analytics.
See how you can start by downloading Confluent, the leading distribution of Apache Kafka and the most powerful enterprise event streaming platform in the industry, or learn more about real-time data streaming.