Big data analytics encompasses modern tools and techniques used to collect, process, and analyze data that is huge in size, fast-changing, diverse, and can generate value for enterprises. Big data is too complex to manage with traditional tools and techniques. In this article, we discuss some important aspects of big data and how to overcome big data analytics challenges using MongoDB.
Big data refers to structured, semi-structured, or unstructured data that is huge not only in Volume but also Velocity and Variety. The three Vs form the core characteristics of big data.
Other than these core characteristics, there are several others that we can consider. Veracity and Value are two additional Vs that are typically taken into account when evaluating the importance of the data for analytics.
V = Other Vs of big data (Vocabulary, Vagueness, Viability ....)
Let’s take a simple example:
User Z shops for a t-shirt online from website C. Unfortunately, the t-shirt Z selects is out of stock.
The website, however, shows similar t-shirts to Z. Z ends up purchasing three t-shirts instead of one. Later, Z also gets an email from website C when the t-shirt they first selected becomes available.
This leads to more sales and happier customers — what more can a business ask for?
But how did this happen?
Over time, website C collects a lot of information (volume) about many users like Z who shop on the site. Since Z uses the same login for many similar transactions — such as food, games, and social media engagement — website C can collect different types of data about the user (variety). Website C gets data at different speeds from different sources — some live feeds, some collected over time (velocity). The website uses algorithms that can analyze this data. This analysis allows website C to get the expected Value:
Big data analytics is the process of analyzing big data to:
This helps businesses to save costs, improve business productivity, increase revenue, and create intelligent organizations.
The structure in which organizations organize the ingestion, processing, and analysis of big data is called big data architecture. Big data architecture ensures high performance, scalability, and choice of tools and technologies for specific use cases.
Some of the many real-world big data analytics use cases are:
Learn more about big data examples and use cases.
Big data analytics power organizations for more efficient operations, intelligent business decisions, and higher profits. This leads to improved company performance:
Big data analytics is complex and requires advanced analytics tools.
Big data analytics tools have several stages that convert data into knowledge and wisdom. The core stages are mentioned below.
Structured or unstructured data can be collected from various sources such as:
Many options exist for storing and integrating data from these diverse sources:
Hadoop and MongoDB can be used together for big data analytics to store, integrate, and process big data in a distributed environment.
Data processing involves organizing and splitting the stored data for analytics. There are two main types of data for processing:
Batch: Batch processing is useful when decision-making is not urgent. Batch processing involves processing blocks of data over a period of time. Data is stored, cleaned, and transformed before performing analytics. Examples include daily operational reports, or fetching user call records to calculate charges.
Stream: Streaming data refers to data that is continuously generated, forming a data stream — either in unstructured or semi-structured form. Stream processing focuses on cleaning and processing the data stream over a sliding time window to get insights and take immediate actions. Data processing happens in smaller chunks, reducing the time between collection and analysis. For example:
The above are examples of time-series data, which is one of the most common types of streaming data. Processing time-series data is usually expensive and complex because it is continuous in nature and has to be in order of (sorted by) time. MongoDB 5.0 introduces native support for time-series data, which makes working with time-series data easier, faster, and lower cost.
The data we receive may contain a lot of duplicates, missing values, outliers, extra spaces, and other such inconsistencies. The data may also need to be reformatted.
Big data engineers use statistical and data transformation tools to clean and transform data. Cleaning can be the most time-consuming task in the entire big data lifecycle.
Data analysis is usually performed with a specific problem statement in hand. Based on that, analysts use the right set of algorithms and analytical Big Data technologies. Some popular big data analytics techniques are:
Depending on the business use case, we can perform different types of big data analytics:
Big data requires tools and technologies for storage, mining, analytics, and visualization. Some popular big data technologies are:
For knowledge discovery, we need specialized data mining tools. Tools like RapidMiner, ElasticSearch etc. help find trends and patterns in big data.
Spark is a top open-source tool for batch and streaming data processing and analytics. R and Python also offer rich libraries to perform advanced analytics.
Blockchain analytics is also gaining popularity in terms of discovering useful information on blockchain data. Blockchain is a decentralized, distributed public ledger that can track and analyze data changes in real time, ensuring data quality and security.
Through big data analysis tools like Excel, Tableau, MongoDB Charts, and Plotly, we can visualize data as charts. The tools share insights and reports with business analysts and stakeholders.
Big data analytics can boost company performance and build intelligent systems. First, we need to overcome challenges like:
MongoDB Atlas solves the big data analytics challenges through its many easy-to-use features.
Atlas offers easy storage of data in the cloud and is compatible with all major cloud providers. MongoDB federated queries allow users to perform queries across various MongoDB systems, like multi-cloud clusters, databases, and AWS S3 buckets. In addition, with the MongoDB aggregation pipeline, we can retrieve the desired documents using a single query, thus taking care of data processing.
The online archive feature in Atlas allows users to maintain and query data in both Atlas cluster and Atlas Data Lake, thus reducing cost of data storage and ensuring data quality at all times.
MongoDB Atlas provides various authentication and encryption methods to maintain data security. MongoDB Connector for BI is a great tool to connect with other BI tools and perform big data analytics on the MongoDB Atlas cluster. Using MongoDB Charts, we can easily visualize data patterns, key metrics, and insights.
The ultimate goal of businesses is to increase revenue by providing maximum value. Big data analytics helps companies achieve this goal by:
Companies can use their historical data to perform predictive analytics. This allows them to:
By collecting public data about competitors, businesses can provide better products and services. They can get data through social media handles, blogs, user comments, ratings, surveys, and more.
Using data mining and machine learning techniques, companies can identify trends and patterns. For example, customer shopping preferences, browsing patterns, or items that users buy together.
Retail analytics helps in understanding customer needs and preferences. Companies can create customized discounts, personalized marketing campaigns, and offers. This results in better customer retention. Retail analytics also helps with supply chain and logistics management, as well as inventory management.
By understanding both their customers and competitors, businesses can create new, innovative products that provide more value to customers. They can also improve upon existing products to serve the same purpose.
Having productive employees is crucial to the progress of the company. Big data analytics can find gaps in the employee development process and aid in making decisions for hiring, training, and development of employees.
MongoDB offers high performance and easy data retrieval because of its embedded document-based structure. Through MongoDB MQL and aggregation pipelines, data can be retrieved and analyzed in a single query. Atlas also enables storage of humongous data on the Atlas data lake.
Some MongoDB Atlas Big Data Analytics benefits are:
Big data analytics helps businesses with better decision-making, thereby increasing revenue and sales. Organizations across the world are investing a lot of money into big data analytics but face practical challenges during implementation. These challenges can be handled by the MongoDB Atlas platform. With MongoDB Atlas, organizations are serving more data, more users, and more insights with greater ease, thereby creating more value worldwide.
Top big data analytics jobs require skills including:
Big data analytics can help businesses increase revenues in many ways, including: