BIG DATA INTRODUCTION

1. • Big data is a collection of massive and complex data sets
and data volume that include the huge quantities of data, data
management capabilities, social media analytics and real-time
data.
• Big data analytics is the process of examining large amounts of
data. There exist large amounts of heterogeneous digital data.

2. Challenges

3. Big Data’s Characteristic
1.Volume refers to the size of data generated
and stored in a Big Data system. We’re
talking about the size of data in the petabytes
and exabytes range.
2.Variety entails the types of data that vary in format
and how it is organized and ready for processing. Big
names such as Facebook, Twitter, Pinterest, Google
Ads, CRM systems produce data that can be
collected, stored, and subsequently analyzed.

4. 3.VelocityThe rate at which data accumulates also influences
whether the data is classified as big data or regular data.
4. Value is another major issue that is worth considering. It is not
only the amount of data that we keep or process that is important. It
is also data that is valuable and reliable and data that must be
saved, processed, and evaluated to get insights.
5.Veracity refers to the trustworthiness and quality of the data. If
the data is not trustworthy and/or reliable, then the value of Big Data
remains unquestionable.

5. Types of Big Data
• a) Structured is one of the types of big data and By structured
data, we mean data that can be processed, stored, and retrieved in
a fixed format
• b) Unstructured data refers to the data that lacks any specific form
or structure whatsoever. Email is an example of unstructured data.
Structured and unstructured are two important types of big data.
• c) Semi-structured Semi structured is the third type of big data.
Semi-structured data pertains to the data containing both the
formats mentioned above, that is, structured and unstructured data

6. Analyst Perspective On Data Repositories
Why Do You Need A Data Repository?
A data repository can help businesses fast-track decision-
making by offering a consolidated space to store data critical to
your operations. This segmentation enables easier data access
and troubleshooting and streamlines reporting and analysis.
Types of Data Repositories
1. Data Warehouse
2. Data Lake
3. Data Mart
4. Metadata Repositories
5. Data Cubes

7. Challenges Associated with a Data Repository
1. An increase in data sets can reduce your system’s speed. To
rectify this problem, ensure that the database management
system can scale with data expansion.
2. In case a system crashes, it can negatively impact your data. It’s
best to maintain a backup of all the databases and restrict
access to control the system risk.
3. Unauthorized operators can access sensitive data more quickly if
stored in a single location than if it’s dispersed across numerous
sources. On the contrary, implementing security protocols on a
single data storage location is more accessible than multiple
ones.

8. State Of Practice In Analytics
Descriptive analytics
Descriptive (also known as observation and reporting) is the most basic level of
analytics. Many times, organizations find themselves spending most of their time in this
level.
Diagnostic analytics
Diagnostic analytics is where we get to the why. We move beyond an observation
(like whether the chart is trending up or down) and get to the “what” that is making it
happen.
Predictive analytics
Predictive analytics allows organizations to predict different decisions, test them
for success, find areas of weakness in the business, make more predictions—and so
forth
Prescriptive analytics
Prescriptive analytics builds on predictive by informing decision makers about
different decision choices with their anticipated impact on a specific key performance
indicators

9. Current Analytical Architecture:
1.For data sources to be loaded into the data warehouse, data needs to be well
understood, structured and normalized with the appropriate data type definitions.
2. As a result of this level of control on the EDW(enterprise data warehouse-on server or
on cloud), additional local systems may emerge in the form of departmental
warehouses and local data marts that business users create to accommodate their
need for flexible analysis. However, these local systems reside in isolation, often are
not synchronized or integrated with other data stores and may not be backed up.
3. In the data warehouse, data is read by additional applications across the enterprise
for Bl and reporting purposes.
4. At the end of this workflow, analysts get data from server. Because users generally
are not allowed to run custom or intensive analytics on production databases, analysts
create data extracts from the EDW to analyze data offline in R or other local analytical
tools to store and process critical data, supporting enterprise applications and enabling
corporate reporting activities

10. Comparison Between BI And Data Science

11. Data Analytics Life Cycle