Introduction:

Big Data Analytics is incredibly trending in today’s world because it is helping businesses make important decisions, earn more money, and stay one step ahead of their competitors. In this blog, we will understand Big Data Analytics and how it is assisting businesses.

What is Big Data Analytics  and how it helps

Big Data Analytics is a process that involves analyzing large volumes of data to extract useful information, enabling faster and better decision-making. 

It helps transform data into meaningful insights, contributing to business growth and facilitating important decision-making for companies. Faster and better decision-making is a significant advantage of Big Data Analytics. It converts hidden patterns in data into meaningful insights and uncovers unknown correlations. In business, Big Data Analytics is used to understand customer behaviour patterns, identify where they invest their time the most, and predict the market and future trends. The application of Big Data Analytics spans industries like healthcare, finance, retail, manufacturing, and more, driving growth and success in each.

For Example:

Companies can employ Big Data Analytics to analyze social media data and sentiment to understand public opinion about their products or services. This information can be used to tailor marketing strategies, address customer concerns, and improve brand reputation.

BDA commonly used software Application & How It Works Step-by-Step Guide :

In Big Data Analytics, we use specific technologies and applications in a step-by-step sequence to transform meaningless data into meaningful information. Let’s discuss the process and how it helps turn a group of random data into valuable insights that can assist a company in making important decisions and driving business growth.

1. Data Acquisition

The lifecycle begins with the acquisition of data from various sources, such as databases, sensors, social media, or logs. Data can be structured, unstructured, or semi-structured.

Commonly used software applications: ETL tools, web scraping tools, APIs, log analysis tools, sensor data platforms, and database management systems.

2. Data Preparation

 In the Data Preparation stage of Big Data Analytics, several software applications are commonly used to cleanse, transform, and preprocess the acquired data. It involves tasks like data cleaning, integration, normalization, and feature selection,

ensuring that the data is of high quality and suitable for analysis

Commonly used software applications: ETL tools, data integration platforms, data wrangling tools, data cleaning software, and programming languages like Python and R with libraries like pandas and dplyr.

3. Data Storage

 The prepared data is stored in a suitable infrastructure, which may include data warehouses, data lakes, or distributed file systems like Hadoop HDFS. The choice of storage depends on the volume, velocity, and variety of the data.
Commonly used software applications: for data storage in Big Data Analytics include distributed file systems like Hadoop HDFS, cloud-based storage solutions like Amazon S3 and Google Cloud Storage, and database management systems like Apache Cassandra and MongoDB.

4. Data Processing

Data Processing is a crucial stage where we analyze the stored data to extract valuable insights. We can use different techniques like batch processing, which involves tools like MapReduce, or real-time processing using platforms like Apache Spark or Apache Flink. These methods help us analyze the data efficiently and derive meaningful information.
Commonly used software applications: for data processing in Big Data Analytics include Apache Hadoop, Apache Spark, Apache Flink, and Apache Storm. These frameworks provide distributed computing capabilities and support the parallel processing of large volumes of data for efficient analysis and processing.

5. Data Analysis

 In this phase, Data Analysis is the process of examining data to discover meaningful patterns and trends. It involves using statistical techniques, data mining methods, machine learning algorithms, and predictive modelling to extract valuable insights. By analyzing the data, we can uncover hidden relationships, make predictions, and gain a deeper understanding of the information it contains. These insights help businesses make informed decisions, identify opportunities, and improve their operations. Data Analysis empowers us to make sense of data and leverage its potential for valuable insights and strategic decision-making.

Commonly used software applications: for Data Analysis tools like Python with libraries like pandas, sci-kit-learn, and TensorFlow, as well as software like IBM SPSS and RapidMiner.

6. Data Visualization

 In this phase, Data visualization makes it easier to understand data and helps decision-makers see the bigger picture. It allows them to make informed choices and take action based on the insights gained from the visual representations of the data. It simplifies complex information and enables better understanding, leading to more effective decision-making and actions.

In  Data visualization By using tools and techniques, we can create charts, graphs, dashboards, and interactive visuals that represent the findings of our analysis. Visualizing the data helps us see patterns, trends, and relationships more clearly, enabling us to communicate complex information in a simple and meaningful way. 

Commonly used software applications: for data visualization tools,  in Big Data Analytics include Tableau, Power BI, QlikView, matplotlib, and ggplot. These tools allow users to create interactive charts, graphs, dashboards, and visual representations of data, making it easier to understand and interpret complex information.

7. Interpretation and Decision-making

 In this stage, the generated insights are interpreted and translated into actionable recommendations or strategies.

Decision-makers and stakeholders use these insights to make informed decisions, improve operations, and achieve business growth. 

It’s about using valuable information to guide decision-making and take actions that can optimize processes, enhance performance, and drive success in the business.
Commonly used software applications:  business intelligence tools like Tableau, IBM Cognos, and Microsoft Power BI, as well as advanced analytics platforms like SAS and RapidMiner.

8. Monitoring and Optimization

The final stage involves monitoring the implemented decisions and continuously optimizing the Big Data Analytics process. Feedback mechanisms and performance metrics are used to evaluate the effectiveness of the analytics solution and make necessary improvements.

The Big Data Analytics lifecycle is iterative and continuous, as new data is constantly acquired, processed, and analyzed to derive fresh insights and drive ongoing improvements.

Commonly used software applications: for monitoring and optimization tools in Big Data Analytics include    Apache Kafka, Apache Hadoop YARN, Apache Mesos, and tools like Nagios and Zabbix for monitoring data pipelines and system performance. 

Additionally, cloud-based monitoring solutions like Amazon CloudWatch and Google Cloud Monitoring are also commonly utilized. These tools help in monitoring the data processing workflows, detecting anomalies, ensuring system stability, and optimizing resource allocation for efficient data analysis.

 Big Data Analytics Used

Big Data Analytics can be used for various purposes across different industries. Here are some typical applications of Big Data Analytics:

1. Business Intelligence

Big Data Analytics is like a superpower for businesses. It helps them understand how their operations are doing, how well they’re performing, and what their customers are up to. With this knowledge, they can make smart decisions, track important goals, and use real-time data to make their processes even better.

For example :

Imagine a food company with multiple restaurants. By analyzing data from sales, customer reviews, and social media, they can discover customer preferences, popular dishes, and peak hours. This helps them make informed decisions like menu adjustments and targeted marketing for better business performance

2. Financial Analysis

Big Data Analytics is like a secret weapon for financial institutions. They use it to crunch massive amounts of data about money, markets, and customers. This helps them make smart investment choices, spot trends in the market, manage risks, and stay in line with regulations. 

For example, any corporate company uses Big Data Analytics to analyze financial data and make strategic investment decisions.

3. Healthcare and Life Sciences

Big Data Analytics is a game-changer in healthcare and life sciences. It takes huge amounts of data from patient records, clinical trials, genetics, and medical images. This helps doctors improve patient care, tailor treatments, and make groundbreaking discoveries. 

For example, a corporate company like Pfizer uses Big Data Analytics to analyze medical data and develop new drugs and treatments.

4. Marketing and Advertising

Big Data Analytics is like a Magical weapon for marketers. It helps them dig into customer info, online chats, social media vibes, and campaign results. This helps create ads that fit you personally and see if they work. 

For example, a big company like Nike uses Big Data Analytics to analyze customer behaviour and preferences, allowing them to deliver personalized ads and measure how effective their marketing campaigns are.

5. Predictive Analytics

It looks at past and current data to predict what’s coming next. Companies use it to forecast sales, prevent customer loss, catch fraud, and maintain equipment before it breaks.

 For example, Amazon uses Predictive Analytics to analyze customer browsing and purchasing patterns, allowing them to recommend products and anticipate customer needs.

These are just a few examples of how Big Data Analytics is used across various industries.

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Before getting into Data Modelling, let’s understand the few terminologies which is the ground for DATA architecting and modeling, which are nothing but OLTP and OLAP.

What is OLTP?

OLTP is nothing but Online Transaction Processing, and we can call this database workload used for transactional systems, which we use to play around with DDL, DML, and DCL.

OLAP is Online Analytical Processing, database workloads are used for modern data warehousing systems, in which we use to play around SELECT queries with simple or complex queries by filtering, grouping, aggregating, and portioning a large data set quickly for reporting/visualization for Data Analyst and Dataset for Data Scientists for specific reasons.

What is Data Modelling

• Data modelling is the well-defined process of creating a data model to store the data in a database or Modren Data warehouse (DWH) system depending on the requirements and focused on OLAP on the cloud system.
• Always this is a conceptual interpretation of Data objects for the Applications or Products.
• This is specifically associated with the different data objects, and the business rules derived to achieve the goals
• It helps in the visual description of data and requires business rules, governing compliances, and government policies on the data like GDPR, PII and etc.,
• It ensures stability in naming conventions, default values, semantics, and security while ensuring the quality of the data.

Data Model

This defines the abstract model that organizes the Description, Semantics, and Consistency constraints of data.

What is really the Data Model underlines on

• What data need for DWH?
• How it should be organized in the DWH system,

DWH Data Model is like an architect’s building plan, which helps to build conceptual models and set a relationship between data-item, let’s say Dimension and Fact, and how they are linked together.

How we could implement DWH Data Modelling Techniques

• Entity-Relationship (E-R) Model
• UML (Unified-Modelling Language)

Consideration Factors for Data Modelling

While deriving the data model, there are several factors that need to be considered, these factors vary based on the different stages of the Data Lifecycle.

• Scope of the Business: There are several departments and diverse business functions around.
• ACID property of the data during transformation and storage.
• Feasibility of the data granularity levels of filtering, aggregation, slicing, and dicing
• Key features of Modern Data Warehouse
• Starts with logical modelling across multi-platforms and an extensive-architecture approach, its enhanced performance, and scalability.
• Serving data for all types and different categories of consumers
  • [Data Scientist, Data Analysts, Downstream applications, API-based system, Data Sharing systems]
• Highly flexible deployment and decoupling approach for cost-effectiveness.
• Well-defined Data Governance model to support quality, visibility, availability security
• Streamlined Master Data Management and Data Catalog and Curation to support functionally and technically.
• Perfect monitoring and tracking of the Data Linage from Source into Serving layer
• Ability to facilitate Batch, Real-Time analysis, and Lambda process of high-velocity, verity, and veracity data.
• Supports Analytics and Advanced Analytics components.
• Agile Delivery approach from Data modelling and delivering aspects to satisfy, their business model.
• Excellent-Hybrid Integration with multiple cloud service providers and maximize the benefits for the customer

Why the Modern DWH be important for us?

Yes! The Modern Data Warehouse systems solve many problems in business challenges

• Data Availability
  Data sources divided across organizations – Certainly, the Modern DWH system allows us to bring the data faster into our table in the form of different ranges and helps to analyze across the organizations, divisions, and behavior. It keeps getting the agility model and stimulates more and more.
• Data Storage
  Data Lakes – In the modern cloud the storage and computation are very flexible and extendable ways, instead of storing in hierarchical files and folders as we used in a traditional data warehouse, a data lake is an extensive repository that holds a massive amount of raw data, and you can store in its native format until required for processing layer.
• Data Maintainability
  As you know that we can’t maintain the historical data in a normal database like RDBMS, there were lots of challenges with respect to querying or fetching the data is a tedious process. So we have to build the DWH with Facts and Dimensions, and we could use the data for data perspective very easily and quickly.
• IoT/ Streaming Data
  Since we’re in the internet world the data flowing across the different applications and Internet of Things data has transformed and based on the business scenarios, needs, etc.

So far, we have discussed the concepts around the Modern DWH system, Let’s move on to data modelling components and techniques.

Data Model Evaluation

Generally, before building the model, each table would undergo the below stages, conceptual, logical, and physical, so exactly in the last stage only we would realize the model as accepted by the business.

Multi-Dimensional Data Modelling Components

The main components are Fact and Dimension tables are the main two tables that are used when designing a data warehouse. The fact table contains the measures of columns and a special key called surrogate, that link to the dimensions tables.

Facts: To define FACTS in one word that is nothing but Measures

It can be measured attributes of the fields, it can be Quantitatively  Measured, and in Numerical Quantities. Generally, it would be a number of orders received and products sold.

Dimensions: It has the attributes and basically “Category Values” or “Descriptive Definition” would be the Product Name, Description, Category, and so on.

Modelling Techniques

For most of the scenarios, while developing the data modelling for DWH, we use to follow the Star Schema or Snowflake Schema, or Kimball’s Dimensional Data Modelling.

Star Schema: This is the most common technique and basic modelling type and is easy to understand. In which Fact table is connected with other all Dimension tables and considerably accepted architectural model and used to develop DWH and Data marts. Each dimension table in the star schema has a Primary-Key and which is related to a Foreign-Key. In the Fact table. joining the tables and querying a little complex and performance a bit slow.

The representation of this model seems like a star with the Fact table at the center and dimensions-tables connecting from all other sides of it, constructing a STAR-like model

Snowflake Schema: This is an extension of the Star Schema with little modification and reduced load and improved performance. here the dimensions tables are normalized into multiple related tables as sub-dimension. So, it minimizes data redundancy. Apparently, it has multiple levels of joins which leads to less query complexity and ultimately improves query performance.

Tables are arranged logically and a many-to-one relationship hierarchy structure and it is resembling a SNOWFLAKE-like pattern. It has more joins between dimension tables, so performance issues might be in place, which leads to the slow query processing times for data retravel.

 Let’s do a quick comparison of Star & Snowflake Schema

Everything is fine with the star schema, as we understood that this is Flexible, Extensible, and many more. But not answered business process and questions from DWH.

Kimball’s answer to below dimensional data modelling.

• The business process to a model – Keeping customer model, product model
• ATOMIC model – Depth of data level stored in the fact table in the concrete ATOMIC model so, we can’t split further for any analysis and not required too
• Building fact tables – designing the fact tables with a strong set of dimensions with all possible categories.
• Numeric facts – Identifying the most important numeric measures use to store at the fact table layer
• The part of the Data Analytics environment where structured data is broken down into low-level components and integrated with other components in preparation for exposure to data consumers

Then why do we need Kimball’s Approach? Obviously, we need them to Expedite the business value and Performance enhancement.

Expedite the business value: When you want to speed to business value, the data needs to be denormalized, so that BI teams can deliver to the business quickly and reliably and improve analytical workloads and performance.

• Bottom-up approach. the DWH is provisioned from the collection of DataMart.
• The Datamart is cooked from OLTP systems that are usually RDBMS and well-tuned with 3NF
• Here the DWH is central to the core model and de-normalized star schema.

Let’s quickly go through Inmon DWH Modelling, it follows a top-down approach. In this model, OLTP systems are a data source for DWH and play as a central repository of data in 3NF. Followed by this Datamart is plugged in and in 3NF. Comparatively with Kimball’s model, this Inmon is not that great option while dealing with BI and AI and data provisioning.

So far, we have discussed various data modelling techniques and their benefits around them.

Data Vault Model (DVM): What had discussed models earlier are predominantly focused on Classical or Modern Data Warehousing and Reporting systems. All we know now is we’re in the digital world delivering a Data Analytics Service to support enterprise-level systems like rich BI, Modern DWH, and Advanced Analytics like Data Science, Machine Learning, and extensive AI. This methodology is an agile way of designing and building modern, efficient and effective DWHs.

DVM is composed of multiple components like Model, Methodology, and Architecture, this is quite different from other DWH modelling techniques in current use. Another way around this is simply we can say that this is NOT a framework, product, and any service, instead, we can say this is Very Consistency, Scalability, highly Flexibility, easily Auditability, and specifically AGILITY. Yes! It is a modern agile way of designing DWH for various systems as mentioned earlier. Along with we can incorporate and implement the standards, policies, and best practices with the help of a well-defined process.

This model consists of three elements Hub, Link, and Satellite.

Hubs: This is one of the core building blocks in DVM. Which is to record a unique list of all the business keys for a single entity. Let’s say, for example, an It may contain a list of all Customer IDs, Employee IDs, Product IDs, and Order IDs in the business.

Links: Is fundamental component in a DVM is Links, which form the core of the raw vault along with other elements Hubs, and Satellites. Generally speaking, this is an association or link, between two business keys in the model. A typical example is Orders and the Customers in the respective table which is associated with customers and orders. And one more I can say store and employee working in store under various department so the link would be link_employee_store

Satellites: In DVM, Satellites connect to other elements in DVM (Hubs or Links). Satellite tables hold attributes related to a link or hub and update them as they change. For example, SAT_EMPLOYEE may feature attributes such as the employee’s Name, Role, Dob, Salary, or Doj. Simply say “The Point in Time Record in the table”. In simple language, we can say Satellites contain data about their parent Hub or Link and Metadata along with when the data has been loaded, from where, and effective business date details. Where the actual data resides for our business entities in the other elements discussed earlier (Hubs and Links).

In DVM architecture each Hub and Link record may have one or more child Satellite records, all the changes to that Hubs or Link.

Pros and Cons

Pros

• This model tracks historical records
• Agile way of building the model as incrementally
• DVM use to provide the facilities of audibility
• Adaptable to changes without re-engineering
• The high degree of parallelism with respect to loads of data
• Supports the fault-tolerant ETL pipelines

Cons

• At a certain point, the models became more complex
• Implementation and understanding of Data Vault are a few challenges
• Since storing historical data capacity storage needed is high
• The model building takes time, so the value to the business is slower than another model

Conclusion

So far, we discussed data and modelling concepts in the below items in detail,

• What are OLTP and OLAP and their major difference?
• What is Data Modelling and what factors influence Data modelling?
• Discussed why the modern DWH is important for us? And various data availability, storage, maintainability, and IoT/ streaming data
• Data Model Evaluation and Data Modelling Components in depth
• Discussed various modelling techniques -Star Schema, Snowflake Schema, Kimball, Inmon, and Data Vault Model, and their components

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Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data.

Big data spans three dimensions: Volume, Velocity and Variety.

Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes—even petabytes—of information.

• Turn 12 terabytes of Tweets created each day into improved product sentiment analysis
• Convert 350 billion annual meter readings to better predict power consumption

Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value.

• Scrutinize 5 million trade events created each day to identify potential fraud
• Analyze 500 million daily call detail records in real-time to predict customer churn faster

Variety: Big data is any type of data – structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together.

• Monitor 100’s of live video feeds from surveillance cameras to target points of interest
• Exploit the 80% data growth in images, video and documents to improve customer satisfaction

Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach. Until now, there was no practical way to harvest this opportunity. Today, IBM’s platform for big data uses state of the art technologies including patented advanced analytics to open the door to a world of possibilities.

There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

We at EITAcies exploring the strategies and technologies to bring the Big Data Platform to the Enterprise