BIG DATA
what is BIG DATA :
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are to'o large or complex to be dealt with by traditional data-processing application software.
Big data is a term used to describe the collection, processing and availability of huge volumes of streaming data in real-time. The three V’s are volume, velocity and variety with credit to Doug Laney). Companies are combining marketing, sales, customer data, transactional data, social conversations and even external data like stock prices, weather and news to identify correlation and causation statistically valid models to help them make more accurate decisions.
Why Is Big Data Different?
In the old days… you know… a few years ago, we would utilize systems to extract, transform and load data (ETL) into giant data warehouses that had business intelligence solutions built over them for reporting. Periodically, all the systems would backup and combine the data into a database where reports could be run and everyone could get insight into what was going on.
The problem was that the database technology simply couldn’t handle multiple, continuous streams of data. It couldn’t handle the volume of data. It couldn’t modify the incoming data in real-time. And reporting tools were lacking that couldn’t handle anything but a relational query on the back-end. Big Data solutions offer cloud hosting, highly indexed and optimized data structures, automatic archival and extraction capabilities, and reporting interfaces have been designed to provide more accurate analyses that enable businesses to make better decisions.
Better business decisions mean that companies can reduce the risk of their decisions, and make better decisions that reduce costs and increase marketing and sales effectiveness.
THE GENERAL EXAMPLES OF BIG DATA:
- Discovering consumer shopping habits.
- Personalized marketing.
- Fuel optimization tools for the transportation industry.
- Monitoring health conditions through data from wearables.
- Live road mapping for autonomous vehicles.
- Streamlined media streaming.
GENERAL APPLICATIONS OF BIG DATA:
Big data applications are applied in various fields like banking, agriculture, chemistry, data mining, cloud computing, finance, marketing, stocks, healthcare etc.
VARIOUS TYPES OF BIG DATA:
- Structured
- Unstructured.
- Semi-structured
STRUCTURED:
The term structured data generally refers to data that has a defined length and format for big data. Examples of structured data include numbers, dates, and groups of words and numbers called strings. Structured data is the data you're probably used to dealing with. It's usually stored in a database.
UNSTRUCTURED:
Unstructured data is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well.
SEMI STRUCTURED:
Semi-structured data is a form of structured data that does not conform with the formal structure of data models associated with relational databases or other forms of data tables, but nonetheless contain tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data.
GENERAL CHARACTERSTICS OF BIG DATA:
Big data can be described by the following characteristics:
- Volume
- Variety
- Velocity
- Variability
(i) Volume – The name Big Data itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon the volume of data. Hence, 'Volume' is one characteristic which needs to be considered while dealing with Big Data.
(ii) Variety – The next aspect of Big Data is its variety.
Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Nowadays, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining and analyzing data.
(iii) Velocity – The term 'velocity' refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data.
GENERAL BENIFITS OF BIG DATA?
Informatica walks through the risks and opportunities associated with leveraging big data in corporations.
- Big Data is Timely – 60% of each workday, knowledge workers spend attempting to find and manage data.
- Big Data is Accessible – Half of senior executives report that accessing the right data is difficult.
- Big Data is Holistic – Information is currently kept in silos within the organization. Marketing data, for example, might be found in web analytics, mobile analytics, social analytics, CRMs, A/B Testing tools, email marketing systems, and more… each with focus on its silo.
- Big Data is Trustworthy – 29% of companies measure the monetary cost of poor data quality. Things as simple as monitoring multiple systems for customer contact information updates can save millions of dollars.
- Big Data is Relevant – 43% of companies are dissatisfied with their tools ability to filter out irrelevant data. Something as simple as filtering customers from your web analytics can provide a ton of insight into your acquisition efforts.
- Big Data is Secure – The average data security breach costs $214 per customer. The secure infrastructures being built by big data hosting and technology partners can save the average company 1.6% of annual revenues.
- Big Data is Authoritive – 80% of organizations struggle with multiple versions of the truth depending on the source of their data. By combining multiple, vetted sources, more companies can produce highly accurate intelligence sources.
- Big Data is Actionable – Outdated or bad data results in 46% of companies making bad decisions that can cost billions.
- Businesses can utilize outside intelligence while taking decisions
Access to social data from search engines and sites like facebook, twitter are enabling organizations to fine tune their business strategies.
- Improved customer service
Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
- Early identification of risk to the product/services, if any
- Better operational efficiency
Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of Big Data technologies and data warehouse helps an organization to offload infrequently accessed data.
VARIOUS ADVANTAGE OF BIG DATA:
- One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks.
- By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services.
- Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money.
- Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, the can understand better where to invest their time and money.
- With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behavior to understand the customer trends and provide a highly ‘personalized’ experience to them.
VARIOUS APPLICATIONS OF BIG DATA:
1) Healthcare
Big Data has already started to create a huge difference in the healthcare sector. With the help of predictive analytics, medical professionals and HCPs are now able to provide personalized healthcare services to individual patients. Apart from that, fitness wearables, telemedicine, remote monitoring – all powered by Big Data and AI – are helping change lives for the better.
2) Academia
Big Data is also helping enhance education today. Education is no more limited to the physical bounds of the classroom – there are numerous online educational courses to learn from. Academic institutions are investing in digital courses powered by Big Data technologies to aid the all-round development of budding learners.
3) Banking
The banking sector relies on Big Data for fraud detection. Big Data tools can efficiently detect fraudulent acts in real-time such as misuse of credit/debit cards, archival of inspection tracks, faulty alteration in customer stats, etc.
4) Manufacturing
According to TCS Global Trend Study, the most significant benefit of Big Data in manufacturing is improving the supply strategies and product quality. In the manufacturing sector, Big data helps create a transparent infrastructure, thereby, predicting uncertainties and incompetencies that can affect the business adversely.
5) IT
One of the largest users of Big Data, IT companies around the world are using Big Data to optimize their functioning, enhance employee productivity, and minimize risks in business operations. By combining Big Data technologies with ML and AI, the IT sector is continually powering innovation to find solutions even for the most complex of problems.
6. Retail
Big Data has changed the way of working in traditional brick and mortar retail stores. Over the years, retailers have collected vast amounts of data from local demographic surveys, POS scanners, RFID, customer loyalty cards, store inventory, and so on. Now, they’ve started to leverage this data to create personalized customer experiences, boost sales, increase revenue, and deliver outstanding customer service.Retailers are even using smart sensors and Wi-Fi to track the movement of customers, the most frequented aisles, for how long customers linger in the aisles, among other things. They also gather social media data to understand what customers are saying about their brand, their services, and tweak their product design and marketing strategies accordingly.
7. Transportation
Big Data Analytics holds immense value for the transportation industry. In countries across the world, both private and government-run transportation companies use Big Data technologies to optimize route planning, control traffic, manage road congestion, and improve services. Additionally, transportation services even use Big Data to revenue management, drive technological innovation, enhance logistics, and of course, to gain the upper hand in the market.
Big Data Analytics Tools
Since the compute, storage, and network requirements for working with large data sets are beyond the limits of a single computer, there is a need for paradigms and tools to crunch and process data through clusters of computers in a distributed fashion. More and more computing power and massive storage infrastructure are required for processing this massive data either on-premise or, more typically, at the data centers of cloud service providers.
In addition to the required infrastructure, various tools and components must be brought together to solve big data problems. The Hadoop ecosystem is just one of the platforms helping us work with massive amounts of data and discover useful patterns for businesses.
VARIOUS TOOL USED IN BIG DATA:
- MapReduce: MapReduce is a distributed computing paradigm developed to process vast amount of data in parallel by splitting a big task into smaller map and reduce oriented tasks.
- HDFS: The Hadoop Distributed File System is a distributed storage and file system used by Hadoop applications.
- YARN: The resource management and job scheduling component in the Hadoop ecosystem.
- Spark: A real-time in-memory data processing framework.
- PIG/HIVE: SQL-like scripting and querying tools for data processing and simplifying the complexity of MapReduce programs.
- HBase, MongoDB, Elasticsearch: Examples of a few NoSQL databases.
- Mahout, Spark ML: Tools for running scalable machine learning algorithms in a distributed fashion.
- Flume, Sqoop, Logstash: Data integration and ingestion of structured and unstructured data.
- Kibana: A tool to visualize Elasticsearch data.
CONCLUSION OF BIG DATA
To summarize, we are generating a massive amount of data in our everyday life, and that number is continuing to rise. Having the data alone does not improve an organization without analyzing and discovering its value for business intelligence. It is not possible to mine and process this mountain of data with traditional tools, so we use big data pipelines to help us ingest, process, analyze, and visualize these tremendous amounts of data.
ABOUT REGEX:
Regex is a very good learning platform and i have learnt what is big data and various concept of big data thank you regrex team . the course was really useful and learnt a lot and at this pandemic situation i have learnt various concept in big data . thank u regex team it was a really wonderfull experience . thank u team
All Thanks to REGex Software Services for the Big Data Workshop and also thanking our mentor Tushar Goyal for awesome teaching on big data. Understood lot of concepts of Big Data Tools.
I am feeling Proud for being part of your team and obliged.