Challenges and Current Trends of Big Data Technologies: Part 1
Summary
TLDRThis lecture explores the challenges and current trends in Big Data technologies, focusing on their application in enterprise data warehouses and business intelligence. It highlights the complexities of handling structured, semi-structured, and unstructured data from diverse sources like websites, social media, and IoT devices. Key challenges include data storage, integration, latency, and real-time processing. The lecture also covers the characteristics of Big Data, known as the five Vsβvolume, velocity, variety, veracity, and value. Additionally, it introduces the Big Data technology stack, emphasizing its flexibility, efficiency, and advanced analytics capabilities.
Takeaways
- π‘ Big Data technologies enhance business insights and decision-making but come with significant challenges in enterprise adoption.
- π The lecture covers an introduction to Big Data, enterprise data landscape, Big Data characteristics, and adoption challenges.
- π Large amounts of structured, semi-structured, and unstructured data come from various sources like websites, social media, and sensors.
- π§ Big Data technologies enable the analysis of all available data, which is crucial for making intelligent business decisions.
- π§ The evolving nature of Big Data technologies presents challenges, particularly in adhering to enterprise quality-of-service requirements.
- ποΈ Enterprise data can be categorized into transactional data, observational data, social interaction data, and Enterprise Content data.
- βοΈ Key challenges in Big Data adoption include integrating large volumes of data, managing data latency, and ensuring real-time processing.
- π Data flexibility is crucial in supporting various sources and consumption mechanisms, while accuracy and validation of Big Data remain critical.
- π The five characteristics of Big Data are volume, velocity, variety, veracity, and value, each playing a critical role in enterprise use.
- π§ The Big Data technology stack offers features like flexible schema, real-time processing, advanced analytics, and reliable management capabilities.
Q & A
What are the main challenges in enterprise adoption of Big Data technologies?
-The main challenges include storing, integrating, and linking data, latency between data generation and consumption, flexibility in data in and out, data cleansing and validation, and return on investment.
How does Big Data technology enable better business insights and decisions?
-Big Data technology allows for the analysis of large amounts of structured, semi-structured, and unstructured data from various sources, which helps in making intelligent business decisions.
What are the four types of data in the enterprise data landscape?
-The four types of data are transactional data, observational data, social interaction data, and Enterprise Content data.
What are the five Vs associated with the characteristics of Big Data?
-The five Vs are Volume, Velocity, Variety, Veracity, and Value.
What does Volume in Big Data refer to?
-Volume refers to the huge amounts of data generated every day that contribute to the size of data.
How is Velocity defined in the context of Big Data?
-Velocity refers to the rapid changes in data generated from various devices and sources like RFID tags and sensors.
What is the significance of Variety in Big Data?
-Variety signifies that 80% of the world's data is unstructured and comes from a variety of sources, making the data varied in nature.
Why is Veracity an important characteristic of Big Data?
-Veracity is important because it refers to the accuracy of data, which is crucial for business leaders to trust the information they use for decision-making.
What is the role of Value in Big Data?
-Value is about driving overall business value from various data sources and analyzing them to extract insights.
What are some of the key features of the Big Data technology stack?
-Key features include flexible schema, efficient batch and real-time processing, indexing of distributed data, support for advanced analytics and modeling, ease of management with auto-sharding and partitioning, reliability, high availability, and standard access mechanisms like JDBC, ODBC, JSON, and REST.
What is the importance of data lineage in the enterprise data landscape?
-Data lineage is crucial for understanding the flow of data across the entire supply chain, which is important for data integration and traceability.
How does the evolving nature of Big Data technologies pose challenges to quality of service requirements?
-The evolving nature of Big Data technologies can make it difficult for enterprises to adhere to quality of service requirements due to the need for continuous adaptation and updates to keep up with new developments.
Outlines
π Big Data Challenges and Trends
This paragraph introduces the lecture's focus on the challenges and current trends in Big Data technologies. It emphasizes the importance of Big Data in enhancing business insights and decision-making through the use of enterprise data warehouses and business intelligence. However, it also highlights significant challenges in enterprise adoption of Big Data, which require the right tools and strategies. The lecture promises to cover an introduction to Big Data, the enterprise data landscape, characteristics of Big Data, adoption challenges, and current trends. It points out the increasing volume of structured, semi-structured, and unstructured data from various sources like websites, billing systems, ERP, CRM, RFID, sensors, and social media platforms. The analysis of this data is crucial for intelligent business decisions. The paragraph also discusses the evolving nature of Big Data technologies and the challenges it poses in maintaining quality of service requirements. It outlines the enterprise data landscape, mentioning four types of data: transactional, observational, social interaction, and enterprise content. Challenges such as storing, integrating, and linking data, latency issues, data flexibility, data cleansing and validation, and return on investment are also discussed.
π οΈ Key Features of Big Data Technology Stack
The second paragraph delves into the use cases and experiments being conducted with Big Data analytics, emphasizing the extraction of value from diverse data sources. It then discusses the key features of the Big Data technology stack, which includes a flexible schema, efficient batch and real-time processing, and indexing of distributed data. The stack supports advanced analytics and modeling and is designed for ease of management with features like auto-sharding and partitioning. It is also reliable, highly available, and provides standard access mechanisms through JDBC, ODBC, JSON, and REST. The paragraph concludes by indicating that the next part of the lecture will detail the various layers of the technology stack, inviting the audience to stay tuned for further insights.
Mindmap
Keywords
π‘Big Data
π‘Enterprise Data Warehouse
π‘Business Intelligence
π‘Data Sources
π‘Data Lineage
π‘Latency
π‘Data Flexibility
π‘Data Cleansing and Validation
π‘Return on Investment (ROI)
π‘Big Data Characteristics
π‘Big Data Technology Stack
Highlights
Big Data technologies are crucial for better business insights and decisions in enterprise data warehouses and business intelligence.
Enterprise adoption of Big Data faces significant challenges that necessitate the right tools and strategies.
Big Data enables the analysis of structured, semi-structured, and unstructured data from diverse sources like websites, billing systems, and social media.
The evolving nature of Big Data technologies presents challenges in maintaining quality of service requirements.
Enterprise data management must cater to a landscape that includes transactional, observational, social interaction, and enterprise content data.
Storing, integrating, and linking high volumes of data with existing enterprise systems is a major challenge.
Latency issues exist between data generation and availability for consumption, emphasizing the need for real-time data processing.
Flexibility in data handling is crucial, supporting various data sources and consumption mechanisms.
Data cleansing and validation are essential for ensuring the accuracy of big data analysis and matching with social data.
Return on investment is a key factor, considering the costs of storing large volumes of data and generating quality business insights.
Big Data is characterized by volume, velocity, variety, veracity, and value.
Volume refers to the massive amounts of data generated daily, contributing to the big data challenge.
Velocity indicates the rapid changes in data from devices and sources like RFID tags and sensors.
Variety describes the nature of data that is 80% unstructured and comes from various sources.
Veracity is about the trustworthiness of information used in business decision-making, highlighting the importance of data accuracy.
Value is derived from analyzing data from various sources to drive overall business value.
Big Data technology stack features include flexible schema, efficient batch and real-time processing, and indexing of distributed data.
The technology stack supports advanced analytics and modeling, ease of management, and standard access mechanisms.
Reliability, high availability, and standard access via JDBC, ODBC, JSON, and REST are part of the Big Data technology stack.
Transcripts
in this lecture I'm going to talk about
the challenges and current trends of Big
Data technologies the use of Big Data
technologies in enterprise data
warehouse and business intelligence
results in better business insights and
decisions however there are significant
challenges in enterprise adoption of big
data that require right set of tools and
adoption strategies in this lecture I'm
going to give you an introduction of Big
Data then I'm going to talk about
enterprise data landscape then I will
discuss various characteristics of Big
Data then I will talk about Big Data
adoption challenges in the enterprise
and finally I will discuss the current
trends in Big Data technologies large
amounts of structured semi-structured
and unstructured data is getting awkward
everyday if you look at the figure you
will notice that the data sources are
from various websites Billings systems
enterprise resource planning customer
relationship management RFID and sensors
network switches and routers and not
only that there are also sources of data
coming from social media that includes
Twitter Facebook and LinkedIn analyzing
this kind of data is becoming extremely
useful for making intelligent business
decisions Big Data technologies makes it
possible to analyze all available data
still evolving nature of Big Data
technologies poses challenges in
adhering to the quality of service
requirements any enterprise needs to
cater for enterprise data management now
let me give you an overview of
enterprise data landscape there are four
types of data they are transactional
data observational data social
interaction data and Enterprise Content
data some data are processed
real time and some data are processed in
batches now let me discuss enterprise
data landscape challenges the first
challenge is storing integrating and
linking data every day high volume of
data is being accurate integrating
seamlessly with existing enterprise is a
big challenge data lineage across the
entire supply chain is an important
issue
the next important challenge is latency
there is a high latency between the time
data is generated and the time data is
available for consumption also there is
a need for real-time processing of data
the third challenge is flexibility in
data in and data out supporting various
type of data sources along with the
ability to support any type of data
consumption mechanisms are important
issues the next challenge is cleanse and
validate big data accuracy and entity
matching with social data and
standardization of machine data is an
important issue cleans and match final
results of big data analysis before
reporting is also an important factor
finally return of investment the cost of
storing high volume of data along with
generating business insights of quality
and in quick turnaround time are
important factors the next thing I want
to talk about are some of the key
characteristics of Big Data there are
five ways that are associated with the
characteristics of big big data and they
are volume velocity variety velocity and
value as we all know that huge amounts
of data is generated every day that
contributes to the volume the next one
is velocity data generated from various
devices and sources like RFID tags and
sensors are changing rapidly
that contributes to the velocity aspects
of big data
third one is variety 80% of the world's
data is unstructured and they come from
a variety of sources
hence this data is varied in nature the
fourth one is veracity one in three
business leaders don't trust the
information they use to make decisions
accuracy of data is an important factor
finally Val driving overall business
value from various data sources and
analyzing them is an important
characteristics of big data
there are various use cases that needs
to be analyzed and there are various
experiments that are going on from all
the different sources of data applying
big data analytics and other tools value
can be extracted now let me discuss some
of the key features of Big Data
technology stack some of the key
features includes flexible schema
efficient batch and real-time processing
and indexing of distributed data it also
supports advanced analytics and modeling
along with ease of management with Auto
sharding and partitioning this Big Data
technology stacks is also reliable and
highly available it also provides
standard access mechanism to JDBC ODBC
JSON rest and important tools in the
next part of this lecture I am going to
talk about in details about all the
layers of the technology stack please
stay tuned for the next lecture
you
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