Challenges and Current Trends of Big Data Technologies: Part 1

Summary

This 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 Vs—volume, velocity, variety, veracity, and value. Additionally, it introduces the Big Data technology stack, emphasizing its flexibility, efficiency, and advanced analytics capabilities.

Takeaways

• 💡 Big Data technologies enhance business insights and decision-making but come with significant challenges in enterprise adoption.
• 🔍 The lecture covers an introduction to Big Data, enterprise data landscape, Big Data characteristics, and adoption challenges.
• 📊 Large amounts of structured, semi-structured, and unstructured data come from various sources like websites, social media, and sensors.
• 🧠 Big Data technologies enable the analysis of all available data, which is crucial for making intelligent business decisions.
• 🚧 The evolving nature of Big Data technologies presents challenges, particularly in adhering to enterprise quality-of-service requirements.
• 🏗️ Enterprise data can be categorized into transactional data, observational data, social interaction data, and Enterprise Content data.
• ⚙️ Key challenges in Big Data adoption include integrating large volumes of data, managing data latency, and ensuring real-time processing.
• 🔄 Data flexibility is crucial in supporting various sources and consumption mechanisms, while accuracy and validation of Big Data remain critical.
• 📈 The five characteristics of Big Data are volume, velocity, variety, veracity, and value, each playing a critical role in enterprise use.
• 🔧 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

00:00

📊 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.

05:02

🛠️ 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.

Keywords

💡Big Data

Big Data refers to the massive volume of structured, semi-structured, and unstructured data that is too large and complex to be processed by traditional data management tools. It is central to the video's theme as it discusses the challenges and trends in Big Data technologies and their impact on enterprise data warehousing and business intelligence. The script mentions that analyzing Big Data can lead to better business insights and decisions, highlighting its significance in the modern data-driven business landscape.

💡Enterprise Data Warehouse

An Enterprise Data Warehouse (EDW) is a system used to report and analyze data so that business decisions can be made. It relates to the video's theme by being a key component where Big Data technologies are applied to store and manage large volumes of data. The script discusses how Big Data technologies can enhance the capabilities of EDWs, enabling better business insights through the analysis of integrated data from various sources.

💡Business Intelligence

Business Intelligence (BI) is the process of analyzing data to help organizations make better decisions. In the context of the video, BI is discussed as a beneficiary of Big Data technologies, which can provide deeper insights and more informed decisions. The script emphasizes how the use of Big Data in BI can lead to more accurate and timely decision-making within an enterprise.

💡Data Sources

Data Sources are the origins from which data is collected. The video script mentions various data sources such as websites, billing systems, enterprise resource planning (ERP), customer relationship management (CRM), RFID, sensors, and social media platforms. These sources are crucial as they contribute to the vast amounts of data that Big Data technologies aim to analyze and make sense of.

💡Data Lineage

Data Lineage is the recording and tracking of data as it moves through the data supply chain. It is highlighted in the script as an important issue in the enterprise data landscape, particularly when dealing with the high volume of data generated daily. Understanding data lineage is crucial for ensuring data quality and for compliance and security purposes within an organization.

💡Latency

Latency in data processing refers to the delay between the generation of data and its availability for use. The script discusses latency as a significant challenge in enterprise data management, emphasizing the need for real-time processing to reduce this delay and enable timely decision-making based on the most current data.

💡Data Flexibility

Data Flexibility pertains to the ability of systems to handle various types of data sources and data consumption mechanisms. The video script mentions this as a key challenge in the enterprise data landscape, where supporting diverse data sources and formats is essential for comprehensive data analysis and integration within an organization.

💡Data Cleansing and Validation

Data Cleansing and Validation involve the processes of identifying and correcting (or removing) inaccurate or incomplete records from an existing database. The script highlights the importance of these processes in ensuring the accuracy and reliability of data, which is critical for the effectiveness of Big Data analytics and the quality of business insights derived from it.

💡Return on Investment (ROI)

Return on Investment (ROI) is a performance measure used to evaluate the efficiency of an investment or compare the efficiency of a number of different investments. In the context of the video, ROI is discussed in relation to the costs associated with storing large volumes of data and generating business insights. It underscores the importance of evaluating the financial benefits of Big Data technologies against their implementation and operational costs.

💡Big Data Characteristics

The characteristics of Big Data, often remembered by the acronym V's, include Volume, Velocity, Variety, Veracity, and Value. These are detailed in the script as the defining aspects of Big Data. Volume refers to the large amounts of data generated daily, Velocity to the speed at which data is produced and changes, Variety to the different types of data (structured, semi-structured, unstructured), Veracity to the trustworthiness of the data, and Value to the ability to extract meaningful insights from the data. These characteristics are central to understanding the nature and challenges of Big Data.

💡Big Data Technology Stack

The Big Data Technology Stack refers to the collection of frameworks, tools, and technologies used to handle, process, and analyze Big Data. The script mentions key features such as flexible schema, efficient batch and real-time processing, indexing of distributed data, support for advanced analytics and modeling, ease of management, and standard access mechanisms. These features are crucial for organizations to effectively leverage Big Data technologies to gain business insights.

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