#Semi- & Unstructured Data Masking

[innovativeroutinesinternational]

Structured, Semi- & Unstructured Data Masking

DarkShield classifies, finds, and deletes PII in RDBs and flat files, too, plus: free text, JSON, XML, HL7/X12, Parquet and log files; MS Office (Word, Excel and Powerpoint) and PDF documents, NoSQL DBs, as well as DICOM and other image formats. Visit Us: https://www.iri.com/products/iri-data-protector

[hanasatoblogs]

Overcoming the Challenges of Big Data: A Deep Dive into Key Big Data Challenges and Solutions

Introduction

Big data has become the backbone of decision-making for businesses, governments, and organizations worldwide. With the exponential growth of data, organizations can harness valuable insights to enhance operations, improve customer experiences, and gain a competitive edge. However, big data challenges present significant hurdles, ranging from data storage and processing complexities to security and compliance concerns. In this article, we explore the key challenges of big data and practical solutions for overcoming them.

Key Challenges of Big Data and How to Overcome Them

1. Data Volume: Managing Large-Scale Data Storage

The Challenge: Organizations generate vast amounts of data daily, making storage, management, and retrieval a challenge. Traditional storage systems often fail to handle this scale efficiently.

The Solution:

Implement cloud-based storage solutions (e.g., AWS, Google Cloud, Microsoft Azure) for scalability.

Use distributed file systems like Hadoop Distributed File System (HDFS) to manage large datasets.

Optimize storage using data compression techniques and tiered storage models to prioritize frequently accessed data.

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2. Data Variety: Integrating Diverse Data Sources

The Challenge: Data comes in various formats—structured (databases), semi-structured (XML, JSON), and unstructured (videos, social media, emails). Integrating these formats poses a challenge for seamless analytics.

The Solution:

Adopt schema-on-read approaches to process diverse data without requiring predefined schemas.

Leverage ETL (Extract, Transform, Load) tools like Apache Nifi and Talend for seamless data integration.

Use NoSQL databases (MongoDB, Cassandra) to manage unstructured data effectively.

3. Data Velocity: Handling Real-Time Data Streams

The Challenge: Organizations need to process and analyze data in real time to respond to customer behavior, detect fraud, or optimize supply chains. Traditional batch processing can’t keep up with high-speed data influx.

The Solution:

Utilize streaming analytics platforms like Apache Kafka, Apache Flink, and Spark Streaming.

Implement event-driven architectures to process data as it arrives.

Optimize data pipelines with in-memory computing for faster processing speeds.

Read - What Is IDQ Ultinfull Mins? A Deep Dive into Informatica Data Quality for Industry Professionals

4. Data Quality and Accuracy

The Challenge: Poor data quality—caused by duplication, incomplete records, and inaccuracies—leads to misleading insights and flawed decision-making.

The Solution:

Deploy automated data cleansing tools (e.g., Informatica Data Quality, Talend).

Establish data governance frameworks to enforce standardization.

Implement machine learning algorithms for anomaly detection and automated data validation.

5. Data Security and Privacy Concerns

The Challenge: With increasing cybersecurity threats and stringent data privacy regulations (GDPR, CCPA), businesses must safeguard sensitive information while maintaining accessibility.

The Solution:

Implement end-to-end encryption for data at rest and in transit.

Use role-based access control (RBAC) to restrict unauthorized data access.

Deploy data anonymization and masking techniques to protect personal data.

Read - Master Data Management in Pharma: The Cornerstone of Data-Driven Innovation

6. Data Governance and Compliance

The Challenge: Organizations struggle to comply with evolving regulations while ensuring data integrity, traceability, and accountability.

The Solution:

Establish a centralized data governance framework to define policies and responsibilities.

Automate compliance checks using AI-driven regulatory monitoring tools.

Maintain detailed audit logs to track data usage and modifications.

7. Scalability and Performance Bottlenecks

The Challenge: As data volumes grow, traditional IT infrastructures may fail to scale efficiently, leading to slow query performance and system failures.

The Solution:

Implement scalable architectures using containerized solutions like Kubernetes and Docker.

Optimize query performance with distributed computing frameworks like Apache Spark.

Use load balancing strategies to distribute workloads effectively.

Read - How to Implement Customer Relationship Management (CRM): A Comprehensive Guide to Successful CRM Implementation

8. Deriving Meaningful Insights from Big Data

The Challenge: Extracting actionable insights from massive datasets can be overwhelming without proper analytical tools.

The Solution:

Leverage AI and machine learning algorithms to uncover patterns and trends.

Implement data visualization tools like Tableau and Power BI for intuitive analytics.

Use predictive analytics to forecast trends and drive strategic decisions.

Conclusion

While big data challenges can seem daunting, businesses that implement the right strategies can transform these obstacles into opportunities. By leveraging advanced storage solutions, real-time processing, AI-driven insights, and robust security measures, organizations can unlock the full potential of big data. The key to success lies in proactive planning, adopting scalable technologies, and fostering a data-driven culture that embraces continuous improvement.

By addressing these challenges head-on, organizations can harness big data’s power to drive innovation, optimize operations, and gain a competitive edge in the digital era.

[govindhtech]

12 New Intel AI Reference Kits: Driving Next-Gen Solutions

12 New Intel AI Reference Kits for a Total of 34 Are Now Available!

The final 12 Intel AI reference kits may now be downloaded for free. To make AI development easier for solutions in consumer goods, energy & utilities, financial services, health & life sciences, manufacturing, retail, and telecommunications, Intel and Accenture developed 34 sets in total. This set completes the collection.

The following are included in each reference kit, which makes use of software improvements for well-known deep learning and machine learning frameworks and libraries including TensorFlow, PyTorch, scikit-learn, and XGBoost:

Data for training

A trained model that is open source

User manuals, libraries, and Intel AI software

To gain a jump start on resolving comparable business issues in your sector, download one or all of them.

The Intel AI reference kits may also be used with your own data.

12 Intel AI reference kits

Synthetic Data Generation  

Synthetic data is being used to meet the need for AI solutions across sectors due to issues with data privacy, restricted data availability, data labeling, inefficient data governance, high cost, and the requirement for a large amount of data.

AI Structured Data Generation (Cross-industry)

Creating a model to artificially produce structured data, such as time series, numerical data, and categorical data, is the main use.

A popular tool in many different sectors, artificial intelligence (AI) structured data generation transforms unstructured or semi-structured data into structured, useful information. Organizations may improve decision-making, optimize operations, and simplify procedures with the use of this data translation.

Text Data Generation  (Cross Industry)

Main Use: Using a large language model (LLM), create synthetic text that resembles the given source dataset.

The process by which algorithms produce meaningful, cohesive, and contextually relevant textual information is known as “text data generation using AI.��� Natural language comprehension, data augmentation, and content production are just a few of the areas in which this technology finds extensive use.

Image Data Generation (Health and Life Sciences)

The main use of picture data generation is the creation of artificial images via the use of generative adversarial networks (GANs). In the health and life sciences, picture data generation is the process of creating or improving photographs for a range of medical and scientific purposes using artificial intelligence and machine learning.

Voice Data Generation (Cross Industry)

Main Use: Using transfer learning with VOCODER models to translate input text data into voice.

Voice data generation is the process of producing life like artificial speech for a range of applications in different sectors. It creates voice recordings that sound human by using AI and machine learning.

AI Data Protection (PII) (Cross Industry)

Main Purpose: Reducing privacy issues with personally identifiable information (PII) throughout the development and design phases.

To satisfy legal requirements and consumer expectations, data masking, data de-identification, and anonymization sanitization are used.

Computational Fluid Dynamics (Cross Industry)

The main purpose of computational fluid dynamics is to accurately simulate fluid flow in order to improve component engineering design, such as in the automotive, energy, and aerospace sectors.

The partial differential Navier Stokes (NS) equations regulating the environment and boundary conditions are usually solved numerically to determine fluid flow profiles. This process is iterative, time-consuming, and requires a lot of computation and memory. The design of a wind turbine blade, a Formula-1 car’s spoiler, or even the arrangement of server chips in a large data center, where wind flow will alter cooling patterns or create hot spots, are examples of infrastructure where these factors discourage the quick design and development of infrastructure where aerodynamics is essential to efficient operation.

Structural Damage Assessment (Cross Industry) 

Main Use: Using satellite-captured photographs as input, a computer-image model is created to determine the extent of damage caused by a natural catastrophe.

Predictive insights and up-to-date knowledge about past or upcoming catastrophes are essential for effective disaster management. Artificial intelligence (AI)-based technical methods for interpreting satellite images of building structural damage hold enormous promise for disaster response and management.

Vertical Search Engine (Cross Industry) 

The main application is the creation of a natural language processing (NLP) model for document semantic search.

Unlike conventional keyword-based search systems, semantic search engines allow the use of the contextual meaning inside a query to discover matched content more intelligently. A technique for converting text-based queries and documents into a format that captures semantic meaning is essential to creating efficient semantic search engines. Compared to conventional text search engines, users may discover answers, information, and goods more precisely using AI-powered semantic vector search.

Data Streaming Anomaly Detection (Cross Industry) 

Creating a deep learning model to identify irregularities in sensor data that tracks equipment conditions is the main application for data streaming anomaly detection.

To handle vast volumes of data as they are created, organizations create apps that integrate streaming data from sensors, meters, mobile devices, social media, machine control systems, etc. More and more professionals are depending on these apps to provide them with up-to-date information so they can do their everyday tasks efficiently.

Visual Process Discovery (Cross Industry)

Main Use: Recording user-workflow interactions in real time and offering unbiased, data-driven insights to improve procedures.

From the provided website screenshots, this Intel AI Reference Kits assists in identifying user interface (UI) components (buttons, links, text, pictures, headers, forms, labels, and iframes) that people have interacted with.

Engineering Design Optimization (Manufacturing)

The main purpose is to assist engineers in creating practical production designs.

Engineers are being pushed by the increasing demands for innovation in manufacturing to use AI models in order to design a variety of complicated, high-performance components, lower manufacturing costs, and speed up the product development process.

Traffic Camera Object Detection (Government)

Main Use: Creating a computer vision model that uses real-time traffic camera picture analysis to forecast the likelihood of auto accidents.

By decreasing road congestion, increasing the precision of pedestrian and vehicle identification, enhancing device-to-device communication, and assisting in the reduction of accidents, AI-enabled traffic camera imaging aids assist in addressing traffic management difficulties.

Find Out More

All 34 of the Intel AI Reference Kits are available for free download and use in your coding pursuits, whether they be personal or professional. Examine them all, download the ones that look helpful, and then distribute the other ones to your coworkers.

Read more on Govindhtech.com

[aakarshanstar]

Innovative Data Engineering for Strategic Decision-Making

Data Engineering Services at Aakarshan Edge

In today’s data-driven landscape, Data Engineering services are pivotal for harnessing the full potential of enterprise data. The complexity and volume of data generated by modern businesses necessitate robust, scalable solutions to transform raw information into actionable insights. At aakarshansedge.com, our Data Engineering services focus on building high-performance data pipelines, architecting data lakes, and enabling seamless integration of disparate data sources, ensuring your business can make informed, real-time decisions backed by data science and analytics.

Key Components of Data Engineering

Data Pipeline Architecture A well-architected data pipeline is the foundation of a successful data engineering strategy. At Aakarshan Edge, we specialize in designing data pipelines that ensure the efficient flow of data from multiple sources into centralized storage solutions. Our pipelines are optimized for speed, reliability, and scalability, handling everything from real-time data streaming to batch processing. We ensure that data is cleansed, transformed, and enriched at each stage to maintain the highest level of accuracy and consistency.

Data Lakes and Warehouses Enterprises today require flexible and scalable storage solutions capable of handling structured, semi-structured, and unstructured data. Aakarshan Edge excels in creating both data lakes and data warehouses solution tailored to your business needs. We implement cloud-native and hybrid solutions that provide the necessary storage capacity and processing power to handle vast amounts of data while offering real-time access for analytics and machine learning applications.

ETL/ELT Process Optimization Extract, Transform, Load (ETL) and its variant, Extract, Load, Transform (ELT), are the backbones of data integration. We optimize ETL/ELT processes to reduce latency and improve efficiency, leveraging automation where possible. Our team uses advanced tools and frameworks to ensure that data transformation is seamless, whether it’s migrating data from legacy systems or integrating with third-party APIs. This results in reduced operational costs, increased performance, and enhanced decision-making capabilities.

Big Data Solutions As big data continues to grow, businesses must find ways to process vast datasets at lightning speed. Aakarshan Edge offers specialized big data solutions, utilizing platforms like Apache Hadoop, Apache Spark, and cloud-based systems such as AWS, Azure, and Google Cloud. Our big data expertise enables us to create scalable infrastructures capable of processing petabytes of data across distributed environments, making data analysis faster, more accurate, and more affordable.

Data Governance and Security Data governance and security are critical concerns in today’s regulatory environment. Aakarshan Edge implements comprehensive data governance frameworks that ensure compliance with international standards such as GDPR and CCPA. We deploy robust security measures, including encryption, access control, and data masking, ensuring that sensitive information is protected at every stage of the data lifecycle. Our proactive approach to data governance helps businesses maintain transparency, reduce risks, and build trust with their customers.

Cloud Data Engineering In the era of cloud computing, businesses increasingly turn to cloud-based data engineering solutions for their flexibility, scalability, and cost-effectiveness. At Aakarshan Edge, we develop cloud-native data architectures using leading platforms like AWS, Google Cloud, and Azure. Our cloud data engineering services include migrating on-premises data to the cloud, optimizing cloud resources for data processing, and building serverless solutions that scale effortlessly with your data needs.

Data Quality Management The value of data lies in its quality. Poor data quality can lead to faulty insights, resulting in bad business decisions. Aakarshan Edge employs sophisticated data quality management strategies to ensure that data is accurate, consistent, and reliable. From automated data validation to anomaly detection and real-time monitoring, we maintain high data integrity across the entire data lifecycle.

AI and Machine Learning Integration To maximize the value of your data, Aakarshan Edge integrates AI and machine learning capabilities into our data engineering solutions. This includes building models for predictive analytics, automating data-driven decision-making, and providing advanced data insights. By leveraging machine learning, businesses can uncover patterns and trends within their data that would otherwise remain hidden, enabling proactive strategies and innovation.

Benefits of Aakarshan Edge’s Data Engineering Services

Scalability and Flexibility: Our data engineering solutions are built to scale with your business, ensuring that as your data needs grow, our systems grow with them. We design modular architectures that allow for easy expansion, whether you’re processing gigabytes or petabytes of data. Cost Efficiency: Through optimization of data processing workflows and cloud resource utilization, we reduce costs while maintaining peak performance. Our solutions prioritize efficiency, allowing businesses to achieve more without overextending budgets. Real-time Insights: With Aakarshan Edge’s real-time data processing capabilities, businesses can react quickly to market changes, customer behavior, and operational inefficiencies. This agility helps companies stay competitive in fast-moving industries. Robust Security: Our security-first approach ensures that all data handled by our systems is protected from breaches, leaks, and unauthorized access. We embed security best practices into every layer of our data engineering services. Custom Solutions: Every business has unique data needs, and at Aakarshan Edge, we tailor our services to meet those specific requirements. From custom-built data lakes to proprietary machine learning models, our solutions are designed for optimal alignment with your business goals.

Conclusion Data is the cornerstone of modern business, and mastering it can unlock significant competitive advantages. Aakarshan Edge provides advanced data engineering services that are designed to meet the complex demands of today’s enterprises. Whether you need to streamline data operations, improve decision-making, or prepare for AI-driven innovations, we have the expertise to turn your data into a powerful business asset. Partner with us to drive your data strategy forward and stay ahead of the curve in an increasingly data-centric world. Contact us (+91-8860691214) (E-Mail: sales@akarshans.com)

[akhil-1]

Azure Data Engineer Training Ameerpet | Azure Data Engineer Training Hyderabad

Analysing Data with Azure Synapse Analytics

Azure Synapse Analytics is a powerful analytics service provided by Microsoft Azure, designed to handle large-scale data processing and analytics tasks. It integrates various components such as data warehousing, big data processing, and data integration, providing a unified experience for data engineers, data scientists, and analysts. Here's how you can analyze data using Azure Synapse Analytics

Azure Data Engineer Training Ameerpet

Data Ingestion: The first step is to ingest your data into Azure Synapse Analytics. This can be done from various sources including Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, Azure Cosmos DB, and more. Azure Synapse Analytics provides connectors and tools to facilitate data ingestion.

Data Preparation: Once the data is ingested, you may need to prepare it for analysis. This involves tasks like cleaning the data, transforming it into a suitable format, and enriching it with additional information. Azure Synapse Analytics provides tools like Azure Data Factory and Azure Data Lake Analytics to perform these tasks.

Data Warehousing: Azure Synapse Analytics includes a data warehousing component that allows you to store structured data in a relational database format optimized for analytical queries. You can create data warehouses using the provisioned resources model or the serverless SQL pool model, depending on your specific requirements.          - Azure Data Engineer Online Training

Big Data Processing: For analyzing large volumes of unstructured or semi-structured data, Azure Synapse Analytics provides integration with Apache Spark. You can use Spark pools to run Spark jobs on large datasets, perform advanced analytics, machine learning, and data exploration.

Data Visualization and Analysis: Azure Synapse Analytics integrates with various visualization tools such as Power BI, Azure Synapse Studio, and Azure Data Studio. These tools allow you to create interactive dashboards, reports, and visualizations to gain insights from your data. You can connect these tools directly to your data stored in Azure Synapse Analytics for real-time analysis.

Advanced Analytics: Azure Synapse Analytics supports advanced analytics scenarios including predictive analytics, machine learning, and artificial intelligence. You can leverage Azure Machine Learning services to build, train, and deploy machine learning models directly from your Synapse workspace.

       Azure Data Engineer Online Training

Security and Governance: Azure Synapse Analytics provides robust security features to ensure the confidentiality, integrity, and availability of your data. This includes role-based access control, encryption at rest and in transit, data masking, and auditing capabilities. You can also enforce compliance standards and regulatory requirements using built-in governance features.

Scalability and Performance Optimization: Azure Synapse Analytics is designed to scale dynamically based on your workload requirements. You can easily scale up or down the resources allocated to your data warehouse or Spark pools to optimize performance and cost-effectiveness.

By leveraging the capabilities of Azure Synapse Analytics, organizations can gain valuable insights from their data, drive data-driven decision-making, and derive business value from their analytics initiatives.

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[jasonsmithbrid23]

A Comprehensive Guide to IoT Data Integration

The Internet of Things (IoT) has revolutionized the way we live and work, by connecting devices, machines, and objects to the internet, enabling them to collect, transmit, and share data. However, the true value of IoT data is only realized when it is integrated and analyzed effectively. This blog post will provide a comprehensive guide to IoT data integration, discussing the challenges, best practices, and solutions for successful implementation.

Challenges in IoT Data Integration:

Data Volume: IoT devices generate a massive amount of data, making it challenging to manage, process, and store.

Data Variety: IoT data comes in various formats, including structured, semi-structured, and unstructured data, making it difficult to integrate.

Data Velocity: IoT data is generated and transmitted in real-time, requiring real-time data processing and integration.

Data Veracity: IoT data may contain errors, inconsistencies, and inaccuracies, making it challenging to ensure data quality.

Data Security: IoT data is sensitive and requires robust security measures to protect against unauthorized access, data breaches, and cyber threats.

Best Practices for IoT Data Integration:

Define a Clear Data Integration Strategy: Develop a clear data integration strategy that aligns with your business goals and objectives.

Select the Right Data Integration Tools: Choose data integration tools that support real-time data processing, data transformation, and data quality checks.

Implement Data Governance: Establish data governance policies and procedures to ensure data accuracy, completeness, and consistency.

Ensure Data Security: Implement robust security measures, including encryption, access controls, and data masking, to protect IoT data.

Monitor and Optimize Data Integration: Continuously monitor and optimize data integration processes to ensure optimal performance and efficiency.

Solutions for IoT Data Integration:

Edge Computing: Implement edge computing to pre-process and filter IoT data closer to the source, reducing data volume and improving data transmission.

Data Virtualization: Use data virtualization to create a unified view of IoT data, regardless of its format or location.

Data Lake: Implement a data lake to store and manage large volumes of IoT data, enabling real-time data processing and analysis.

Streaming Analytics: Use streaming analytics to analyze IoT data in real-time, enabling real-time decision-making and action.

Machine Learning: Leverage machine learning algorithms to identify patterns, trends, and insights in IoT data, enabling predictive analytics and automated decision-making.

Conclusion:

IoT data integration is a critical success factor for leveraging the full potential of IoT. By addressing the challenges, implementing best practices, and using the right solutions, organizations can unlock the power of IoT data, enabling real-time decision-making, predictive analytics, and automated decision-making. With a well-designed IoT data integration strategy, organizations can gain a competitive edge, improve operational efficiency, and drive business growth.

[kdmarketresearch]

Business Intelligence Market Business Strategies Growth Drivers Outlook and Forecast for 2018-2023

A Comprehensive research study conducted by KD Market Research on  Business Intelligence Market Opportunity Analysis and Industry forecast. report offers extensive and highly detailed historical, current and future market trends in Business Intelligence Market. Business Intelligence Market report includes market size, growth drivers, barriers, opportunities, trends and other information which helps to find new opportunities in this market for the growth of the business through new technologies and developments.

The global business intelligence market is expected to mask a CAGR of 10.8% during the projected period. The world is going through great evolutions, this involves rapid urbanization, industrialization and more. Further, this developing world is creating many opportunities for people and is helping businesses to grow fast and bigger then ever. There are infinite number of businesses are running across the globe and this number is likely to increase at remarkable rate in upcoming years. Likely, rising number of businesses is a major factor that is augmenting the demand for numerous business intelligence tools, which in turn projected to bolster the growth of market in near future.

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Segmentation

The research offers a comprehensive analysis of business intelligence market with respect to following sub-markets:

By Component

- Software - Platform - Services - Managed Services - Professional Services

By Data Type

- Structured Data - Semi-Structured Data - Unstructured Data

By Deployment Type

- On-Demand - On-Premises

By Business Size

- Small & Medium Enterprises - Large Enterprises

By Technology

- Cloud BI - Traditional BI - Social BI - Mobile BI

By Application

- Operations Management - Network Management and Optimization - Predictive Asset Maintenance - Sales and Marketing Management - Fraud Prevention and Security Management - Workforce Management - Supply Chain Optimization - Other Applications

By End User

- Media and Entertainment - Manufacturing - Telecommunications and IT - Transportation and Logistics - Retail and Consumer Goods - Banking, Financial Services, and Insurance - Energy and Utilities - Healthcare and Life Sciences - Government and Defense - Others

By Geography

- North America (U.S. & Canada) - Europe (Germany, United Kingdom, France, Italy, Spain, Russia and Rest of Europe) - Asia Pacific (China, India, Japan, South Korea, Indonesia, Taiwan, Australia, New Zealand and Rest of Asia Pacific) - Latin America (Brazil, Mexico, Argentina and Rest of Latin America) - Middle East & Africa (GCC, North Africa, South Africa and Rest of Middle East & Africa)

Competitive Landscape

The report profiles various major market players such as;

- Sisense - Looker Data Sciences - Tableau Software - SAP SE - Domo, Inc. - Microsoft - IBM - QlikTech International AB - Dundas Data Visualization, Inc. - Yellowfin Business Intelligence - Other Prominent Players

Competitive landscape analysis provides detailed strategic analysis of the company’s business and performance such as financial information, revenue breakup by segment and by geography, SWOT Analysis, risk analysis, key facts, company overview, business strategy, key product offerings, marketing and distribution strategies, new product development, and recent news (acquisition, expansion, technology development, research & development expansion and other market activities).

The study also provides company’s positioning and market share in business intelligence market.

Timeline Considered for Analysis:

- 2017- Base Year - 2018 – Estimated Year - 2019 to 2023 – Forecasted Year

Research Scope and Deliverables

- Overview & Executive Summary - Market Drivers, Trends, Challenges and Opportunities - Market Size and Forecast Projections - Macroeconomic Indicators of Various Countries Impacting the Growth of the Market - Extensive Coverage of Industry Players including Recent Product Launches and Market Activities - Porter’s Five Force Analysis

Market Segmentation Analysis: Industry report analyzes the global business intelligence market by the following segments: - Component - Data Type - Technology - Deployment Type - Business Type - Application - End User

Geographic Market Analysis: The report offers separate analysis of North America, Europe, Asia Pacific, Latin America and Middle East & Africa. In addition, further breakdown of market data and analysis of region into countries is covered in the report.

Customization: We also offers customization’s in the industry report as per the company’s specific needs.

Key Questions Answered in the Global Business Intelligence Industry Report

- What is the overall market size in 2017? What will be the market growth during the forecast period i.e. 2018-2023?

- Which region would have high demand for product in the upcoming years?

- What are the factors driving the growth of the market?

- Which sub-market will make the most significant contribution to the market?

- What are the market opportunities for existing and entry-level players?

- What are various long-term and short-term strategies adopted by the market players?

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Table of Content

Market Definition and List of Abbreviations

1. Executive Summary 2. Growth Drivers & Issues in Global Business Intelligence (BI) Market 3. Global Business Intelligence (BI) Market Trends 4. Opportunities in Global Business Intelligence (BI) Market 5. Recent Industry Activities, 2017 6. Porter's Five Forces Analysis 7. Market Value Chain and Supply Chain Analysis 8. Products Average Price Analysis, By Country 9. Global Business Intelligence (BI) Market Value & Forecast (USD Million), 2017-2023

10. Global Business Intelligence (BI) Market Segmentation Analysis, By Component 10.1. Introduction 10.2. Market Attractiveness, By Component 10.3. BPS Analysis, By Component 10.4. Hardware Market Value & Forecast (USD Million), 2017-2023 10.5. Software Market Value & Forecast (USD Million), 2017-2023 10.6. Platform Market Value & Forecast (USD Million), 2017-2023 10.7. Services Market Value & Forecast (USD Million), 2017-2023 10.8. Managed Services Market Value & Forecast (USD Million), 2017-2023 10.9. Professional Services Market Value & Forecast (USD Million), 2017-2023

11. Global Business Intelligence (BI) Market Segmentation Analysis, By Data Type 11.1. Introduction 11.2. Market Attractiveness, By Data Type 11.3. BPS Analysis, By Data Type 11.4. Structured Data Market Value & Forecast (USD Million), 2017-2023 11.5. Semi-Structured Data Market Value & Forecast (USD Million), 2017-2023 11.6. Unstructured Data Market Value & Forecast (USD Million), 2017-2023

12. Global Business Intelligence (BI) Market Segmentation Analysis, By Technology 12.1. Introduction 12.2. Market Attractiveness, By Technology 12.3. BPS Analysis, By Technology 12.4. Cloud BI Market Value & Forecast (USD Million), 2017-2023 12.5. Traditional BI Market Value & Forecast (USD Million), 2017-2023 12.6. Social BI Market Value & Forecast (USD Million), 2017-2023 12.7. Mobile BI Market Value & Forecast (USD Million), 2017-2023

13. Global Business Intelligence (BI) Market Segmentation Analysis, By Deployment 13.1. Introduction 13.2. Market Attractiveness, By Deployment 13.3. BPS Analysis, By Deployment 13.4. On-Demand Market Value & Forecast (USD Million), 2017-2023 13.5. On-Premises Market Value & Forecast (USD Million), 2017-2023

14. Global Business Intelligence (BI) Market Segmentation Analysis, By Business Size 14.1. Introduction 14.2. Market Attractiveness, By Business Size 14.3. BPS Analysis, By Business Size 14.4. Small and Medium-Sized Enterprises Market Value & Forecast (USD Million), 2017-2023 14.5. Large Enterprises Market Value & Forecast (USD Million), 2017-2023

15. Global Business Intelligence (BI) Market Segmentation Analysis, By Application 15.1. Introduction 15.2. Market Attractiveness, By Application 15.3. BPS Analysis, By Application 15.4. Operations Management Market Value & Forecast (USD Million), 2017-2023 15.5. Network Management and Optimization Market Value & Forecast (USD Million), 2017-2023 15.6. Predictive Asset Maintenance Market Value & Forecast (USD Million), 2017-2023 15.7. Sales and Marketing Management Market Value & Forecast (USD Million), 2017-2023 15.8. Fraud Detection and Security Management Market Value & Forecast (USD Million), 2017-2023 15.9. Workforce Management Market Value & Forecast (USD Million), 2017-2023 15.10. Supply Chain Optimization Market Value & Forecast (USD Million), 2017-2023 15.11. Other Applications

Continue….

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At KD Market Insights, we offer our client a deep market research reports accompanied by business consulting services that can help them to reach on top of the corporate world. Our customized reports are built by keeping all factors of the industry in mind.

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[synarionit]

Impact Of AI, IoT And Big Data On Transportation Industry

In the prevailing scenario, humans are innovating various technologies for greater insight into our day-to-day activity so that we can improve our lifestyle by upgrading our processes. Such innovations like AI, IoT and Big Data have made some fascinating and intriguing enhancements to the transportation industry as well.

These cutting edge technologies are driving almost every industry by transforming their concepts. Here, we are going to talk about how AI, IoT, and Big Data are ruling the transportation industry.

Problems in Transportation Industry

Getting around in any big city can be a real pain. Traffic jams seem to be a consistent complaint, and simply getting to work can turn into a burden, even on the best of days.

With more people than ever before flocking to the world’s major urban areas, the problem of crowding and inefficient transportation only stands to get much worse.

Introduction of IoT, Artificial Intelligence and Big Data in Transportation Industry

Artificial intelligence, the Internet of Things(IoT), and Big Data are changing the way everything works in the world. Starting from the transportation industry to our homes, the above technologies are making ripples.

The integration of these technologies in new mobile app development is a big deal for revenue generation. That means, its advantages and possibilities for the future seem endless.

IoT Introduction

Internet of Things (IoT) is an ecosystem of connected physical objects that anyone can access through the internet.

You’ll see this definition everywhere on the Internet. Still not getting it?

Now let us explain you IoT in simple words-

The Internet of Things is actually a pretty simple concept, it means taking all the things around you and connecting them to the internet.

The ‘thing’ in IoT could be a human with a heart monitor or any automobile with built-in-sensors, i.e. objects that have been assigned an IP address and have the capability to transfer data over a network without any manual assistance.

IoT in Transportation Industry

The adoption of IoT within the transportation industry has led to the incorporation of assorted tools & services that facilitate better transport management through-

Traffic congestion control system

Automotive telematics

Reservation System

Toll & ticketing systems

Security and surveillance system

Remote observance & others

Need for IoT in the On Demand Taxi Industry

On-demand taxi firms like Uber and Ola have made a negative impact on the traditional taxi firms which uses taxi dispatch systems for taxi mobile app development. That means the traditional taxi businesses are hanging on the verge.

In 2019, the transportation industry is forecast to invest $71 billion on IoT solutions.

Impact of IoT on Taxi Industry

Cost

Convenience

Speed

These are the 3 main reason why on-demand taxi business is a massive hit. IoT brings all the processes on a centralized cloud network to gather real-time information.

Through the single system, all processes like dispatching, communication, swift service, and payment are integrated.

Likewise, the gap between the passengers and drivers can be eliminated through one-on-one communication, licensed drivers and call masking features in terms of safety.

Let’s look at some stats that will justify the impact of IoT on the transportation market-

According to the report, global IoT in the transportation market valued at $135.35 billion in 2016 and is expected to reach $328.76 billion by 2023.

The highest revenue is generated in North America of $46.75 billion in 2016.

IoT in the transportation segment is growing at a CAGR of 13.1% from 2017 to 2023.

Artificial Intelligence Introduction

Artificial intelligence (AI) is the term in which a computer program or a machine has the ability to think and learn. The AI concept is all based on the idea of building machines having the ability to think, act, and learn just like humans.

Now, let us explain AI into more simpler words-

Artificial intelligence is coined from two different words-

Artificial that is a man-made while,

Intelligence, on the other hand, is the ability of the mind to understand principles, truth, to acquire knowledge, facts or meanings, and apply it to practice.

However, artificial intelligence is machines created by humans to make life easy and comfortable.

Artificial Intelligence in Transportation Segment is expected to value 10.30 Billion USD by 2030

Artificial Intelligence in Transportation Industry

AI applications in the transportation industry are driving the evolution of the next generation of Intelligent Transportation Systems.

Some of the most common aspects where we use in transportation are:

Autonomous Vehicles

Traffic Management Solutions

Smartphone Apps

Passenger Transportation

Law Enforcement

Impact of AI on Transportation Industry

AI in the transport industry has made everything possible from road safety issues to monitoring the fleet management systems. That means Artificial Intelligence(AI) has been an end to end solution provider.

Stats to prove the impact of AI on transportation and logistic Industry-

According to the report, global AI in transportation market size is USD 1.21 billion in 2017 and expected to reach USD 10.30 billion by 2030

It is growing  at a CAGR of 17.87% from 2017 to 2030

Big Data Introduction

Big Data is a term that can describe a large collection of data that is huge in size.  It is still growing exponentially with time.

It has 3 forms-

Structured

Unstructured

Semi-structured

So, Big data has the potential to mine all that data as information and use in many advanced analytics applications.

Big Data in Transportation Industry

Over the past few years, big data has transformed everything. Even the transportation industry, making a daily commute a much more convenient experience.

Big Data is an evolving paradigm and has currently grabbed all the attention of global interest, especially within the transportation industry.

Impact of Big Data in Transportation Industry

Various travel and transportation industry segments, such as airlines, railways, hospitality, and others, have adopted big data analytics in their iPhone or Android app development to manage-

Customer records

Transaction history

Pricing data for better customer feedback

Enable optimized route planning

Detection of networks with poor infrastructure

Calculation of travel delay

Drive the market growth of big data in the taxi market as well

Stats that show Big Data’s growth in the Market-

Big Data market revenues can increase from $42B in 2018 to $103B in 2027

The entire global software market to be worth $628B in revenue.

How AI, IoT, And Big Data Are Ruling The Market

In Tokyo, a group of companies is using location-enriched big data and artificial intelligence (AI) to place cabbies at the intersection of supply and demand, hoping that.

Japan Taxi Co., Ltd. has unveiled the Smart IoT Mobility System, which integrates a full suite of connectivity and communication features in a highly-optimized solution for commercial vehicle apps.

If these technologies are handles and integrated properly, it can be a great help for on-demand solutions like On Demand Food Delivery App, On-demand taxi booking app, on demand grocery delivery apps and On Demand Doctor Appointment app and many more.

Summing-up

Transportation problems become a challenge when the system and users behavior is too difficult to model and predict the traveling patterns.

Technologies like AI, IoT, and Big Data are a big relief to all those challenges that the transportation segment is facing.

IoT, AI and Big Data combined with your robust on-demand taxi booking app is a sure slingshot towards success.

[waaaiz]

Data-centric Security market Research Report

Growth opportunities in the Data-centric Security market look promising over the next six years. This is mainly due to the stringent compliances and regulations, increasing cybertheft incidents, and the mounting demand for cloud-based data indemnity.

Request for a FREE Sample Report on Data-centric Security market

Data-centric Security market Dynamics (including market size, share, trends, forecast, growth, forecast, and industry analysis)

Key Drivers

Several prominent drivers supplementing the growth of the data-centric security market include the escalating demand for cloud-based data protection, rigorous concessions, and rules, and augmented data risk led by the exploitation of big data analytics, Machine Learning (ML), and Artificial Intelligence (AI) technology. The data production rate has accelerated in structured, semi-structured, and unstructured formats. Thus, they are proceeding in the direction of digitalization and data-centric security. It offers an inclusive way to safeguard data compliance and confidentiality. They are implemented to data at rest and data in use and can use enormous protection methods through tokenization and masking, encryption, and data administration and consent. Moreover, these solutions are more concerned about the surveillance and privacy of data rather than the safety of endpoints, networks, and applications. In addition, the rising cases of cyber-attacks are bolstering market growth.

Furthermore, the widespread outbreak of the coronavirus pandemic has restructured the employment of digital technology. Organizations must switch their systems and adapt to the cloud and digital platforms by taking relevant protective measures like perimeter security during the COVID-19. In accordance with the National Broadcasting Company, 33,000 unemployment prospects were exposed to a data security breach from the Pandemic Unemployment Assistance program in May 2020. However, the incomprehension of enterprises related to security violations due to internal vulnerabilities hinders market growth. Moreover, low budget concerns and little knowledge about data security are other factors projected to curb the data-centric security market share.

Regional Drivers

Based on the regional coverage, Asia-Pacific is predicted to witness a higher CAGR during the forecast period. This is primarily attributed to mobile workforce expansion accompanied by the accelerating penetration of mobile devices. They are pliable to endorse these security solutions to secure their crucial and sensitive business data from commercial espionage and cyber threats of computer hackers. APAC countries like China and Japan have extensively acquired encryption technologies to protect data from being stolen.

Data-centric Security market’s leading Manufacturers:

·        Lepide

·        Imperva

·        Cyberdefense

·        Varonis

·        Informatica

·        Micro Focus

·        Talend

·        Forcepoint

·        SECLORE

·        Broadcom

Data-centric Security market Segmentation:

Segmentation by Component

·        Software and Solutions

·        Data discovery and classification

·        Data Protection

·        Data governance and compliance

·        Other

·        Professional Services

Segmentation by Deployment mode

·        On-premise

·        On-cloud

Segmentation by Organization size

·        Small and Medium-sized Enterprises (SMEs)

·        Large Enterprises

Segmentation by Verticals

·        Banking, Financial, Services, and Insurance (BFSI)

·        Government and Defense

·        Healthcare and Pharmaceuticals

·        IT and Enterprises

·        Telecommunciation

·        Retail

·        Other

Segmentation by Region:

·        North America

o   United States of America

o   Canada

·        Asia Pacific

o   China

o   Japan

o   India

o   Rest of APAC

·        Europe

o   United Kingdom

o   Germany

o   France

o   Spain

o   Rest of Europe

·        RoW

o   Brazil

o   South Africa

o   Saudi Arabia

o   UAE

o   Rest of the world (remaining countries of the LAMEA region)

About GMI Research

GMI Research is a market research and consulting company that offers business insights and market research reports for large and small & medium enterprises. Our detailed reports help the clients to make strategic business policies and achieve sustainable growth in the particular market domain. The company's large team of seasoned analysts and industry experts with experience from different regions such as Asia-Pacific, Europe, North America, among others, provides a one-stop solution for the client. Our market research report has in-depth analysis, which includes refined forecasts, a bird's eye view of the competitive landscape, key factors influencing the market growth, and various other market insights to aid companies in making strategic decisions. Featured in the 'Top 20 Most Promising Market Research Consultants' list of Silicon India Magazine in 2018, we at GMI Research are always looking forward to helping our clients to stay ahead of the curve.

Media Contact Company Name: GMI RESEARCH Contact Person: Sarah Nash Email: enquiry@gmiresearch.com Phone: Europe – +353 1 442 8820; US – +1 860 881 2270 Address: Dublin, Ireland Website: www.gmiresearch.com

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Unveiling the Power of ETL Tools: A Comprehensive Guide and ETL Tools List

In the ever-evolving landscape of data management, organizations are continually seeking efficient ways to extract, transform, and load (ETL) their data. ETL tools play a pivotal role in this process, facilitating seamless data integration and ensuring that businesses can harness the full potential of their data. In this article, we'll explore the significance of ETL tools, their key functionalities, and provide a curated ETL tools list for those looking to streamline their data workflows.

Understanding ETL Tools:

ETL, an acronym for Extract, Transform, and Load, refers to the process of collecting data from various sources, transforming it into a suitable format, and loading it into a destination database or data warehouse. ETL tools automate and streamline these tasks, offering a robust solution for organizations dealing with vast and diverse datasets.

Key Functionalities of ETL Tools:

1. Data Extraction:

   ETL tools excel in extracting data from a multitude of sources, including databases, cloud platforms, and flat files. This functionality is crucial for businesses dealing with disparate data spread across different systems.

2. Data Transformation:

   Once data is extracted, ETL tools facilitate its transformation into a consistent and usable format. This involves cleaning, restructuring, and enriching the data to meet the specific requirements of the target system.

3. Data Loading:

   The final step involves loading the transformed data into a destination system, such as a data warehouse. ETL tools ensure that the data is efficiently moved to its intended location, ready for analysis and reporting.

Recommended ETL Tool: IRI Voracity 

One standout ETL tool in the market in terms of speed, features, and affordability is IRI Voracity, offered by Innovative Routines International (IRI), The CoSort Company. Voracity is a comprehenensive data manipulation and integration platform designed to handle large volumes of data efficiently. Let's delve into some of its key features:

1. High-Performance Sorting:

   The IRI CoSort engine in Voracity has been the industry’s fastest sorting engine off the mainframe for decades, speeding up transformation and loading steps in ETL jobs even in other ETL tool environments. This is particularly beneficial when dealing with massive datasets that require quick and efficient processing, and the need for sort-embedded joins and aggregations CoSort also performs at the same time.

2. Data Permutation Capabilities:

   In addition to the versatile data transformation capabilities like sorts, joins and aggregations, users can also (simultaneously) use the engine to: convert and reformat their data types, file formats and database schema for new applications and cloud migrations; filter, cleanse and enrich their data for data quality during the ETL process; transpose (pivot and normalize data) and report on different types of slowly changing dimension; produce custom detail and summary reports, or rapidly wrangle high volumes of data for analytics. This ensures that the data is not only moved seamlessly but is also optimized for the next phase in its lifecycle.

3. Data Discovery, Masking and Testing:

   Security is paramount in the world of data management. Voracity includes PII data classification, discovery, and masking to safeguard sensitive information in its ETL jobs, standalone production sources, and in lower DevOps environments. In addition to database subsetting and smart test data synthesis, these features ensure compliance with data protection regulations and can protect semi- and unstructured data sources, too.

4. Integration with Other Tools:

   In addition to being callable from slower ETL tools, Voracity components seamlessly integrate with various databases, business intelligence tools, and other data engineering and governance platforms like DataSwitch and Quest EDGE. Conversely, their support for IRI metadata means it is easy to run Voracity jobs to speed up and secure your existing tool environment without losing your investment in their metadata.  

Other ETL Tools:

Now that we've explored the capabilities of IRI Voracity, let's broaden our perspective with a curated list of other notable ETL tools that cater to different organizational needs:

1. Apache Nifi:

   An open-source ETL tool that provides a web-based interface for designing data flows, Apache Nifi is particularly well-suited for organizations embracing a scalable and flexible approach to data integration.

2. Talend:

   Talend is a popular open-source ETL tool known for its user-friendly interface and extensive set of connectors. It supports batch and real-time data integration, making it versatile for various business scenarios.

3. Microsoft SQL Server Integration Services (SSIS):

   SSIS is a part of the Microsoft SQL Server database suite, offering a robust ETL solution for organizations invested in the Microsoft ecosystem. It provides a visual design interface for building data integration solutions.

4. Informatica PowerCenter:

   Informatica PowerCenter is a widely used ETL tool that provides advanced data integration capabilities. It offers a scalable and high-performance solution for organizations dealing with complex data integration requirements.

5. Oracle Data Integrator (ODI):

   Oracle's ODI is an ETL tool designed for enterprises using Oracle databases. It provides seamless integration with Oracle systems and supports both batch and real-time data integration.

6. Apache Spark:

   While primarily known as a big data processing framework, Apache Spark includes powerful ETL capabilities. It's suitable for organizations dealing with large-scale data processing and analytics.

Conclusion:

In the data-driven era, ETL tools have become indispensable for organizations aiming to harness the full potential of their data. Whether it's IRI Voracity with its robust data manipulation and security features, or other tools like Apache Nifi, Talend, and Informatica, choosing the right ETL tool depends on the unique needs and requirements of each business.

As you embark on your data integration journey, explore the diverse functionalities offered by these ETL tools and select the one that aligns seamlessly with your organization's objectives. The world of ETL tools is expansive, and with the right solution, you can transform the way you manage and leverage your data for better business outcomes.

[sophiajhon-blog]

Business Intelligence Market is expected to mask a CAGR of 10.8% during 2018-2023

A Comprehensive research study conducted by KD Market Insights on " Business Intelligence Market - By Component (Software, Platform, Services, Managed Services, Professional Services) By Data Type (- Structured Data, Semi-Structured Data, Unstructured Data) By Deployment Type ( On-Demand, On-Premises) By Business Size (Small & Medium Enterprises, Large Enterprises) By Technology (- Cloud BI, Traditional BI, Social BI, Mobile BI) By Application (Operations Management, Network Management and Optimization, Predictive Asset Maintenance, Sales and Marketing Management, Fraud Prevention and Security Management, Workforce Management, Supply Chain Optimization, Other Applications) By End User (Media and Entertainment, Manufacturing, Telecommunications and IT, Transportation and Logistics, Retail and Consumer Goods, Banking, Financial Services, and Insurance, Energy and Utilities, Healthcare and Life Sciences, Government and Defense, Others) & Global Region - Market Size, Trends, Share & Forecast 2018-2023" report offers extensive and highly detailed historical, current and future market trends in the Global and regional/market. The Business Intelligence Market report includes market size, growth drivers, barriers, opportunities, trends and other information which helps to find new opportunities in this market for the growth of the business through new technologies and developments. The global business intelligence market is expected to mask a CAGR of 10.8% during the projected period. The world is going through great evolutions, this involves rapid urbanization, industrialization and more. Further, this developing world is creating many opportunities for people and is helping businesses to grow fast and bigger then ever. There are infinite number of businesses are running across the globe and this number is likely to increase at remarkable rate in upcoming years. Likely, rising number of businesses is a major factor that is augmenting the demand for numerous business intelligence tools, which in turn projected to bolster the growth of market in near future. Request for Sample @ https://www.kdmarketinsights.com/sample/295 Segmentation The research offers a comprehensive analysis of business intelligence market with respect to following sub-markets: By Component - Software - Platform - Services - Managed Services - Professional Services By Data Type - Structured Data - Semi-Structured Data - Unstructured Data By Deployment Type - On-Demand - On-Premises By Business Size - Small & Medium Enterprises - Large Enterprises By Technology - Cloud BI - Traditional BI - Social BI - Mobile BI By Application - Operations Management - Network Management and Optimization - Predictive Asset Maintenance - Sales and Marketing Management - Fraud Prevention and Security Management - Workforce Management - Supply Chain Optimization - Other Applications By End User - Media and Entertainment - Manufacturing - Telecommunications and IT - Transportation and Logistics - Retail and Consumer Goods - Banking, Financial Services, and Insurance - Energy and Utilities - Healthcare and Life Sciences - Government and Defense - Others By Geography - North America (U.S. & Canada) - Europe (Germany, United Kingdom, France, Italy, Spain, Russia and Rest of Europe) - Asia Pacific (China, India, Japan, South Korea, Indonesia, Taiwan, Australia, New Zealand and Rest of Asia Pacific) - Latin America (Brazil, Mexico, Argentina and Rest of Latin America) - Middle East & Africa (GCC, North Africa, South Africa and Rest of Middle East & Africa) Competitive Landscape The report profiles various major market players such as: - Sisense - Looker Data Sciences - Tableau Software - SAP SE - Domo, Inc. - Microsoft - IBM - QlikTech International AB - Dundas Data Visualization, Inc. - Yellowfin Business Intelligence - Other Prominent Players Competitive landscape analysis provides detailed strategic analysis of the company’s business and performance such as financial information, revenue breakup by segment and by geography, SWOT Analysis, risk analysis, key facts, company overview, business strategy, key product offerings, marketing and distribution strategies, new product development, and recent news (acquisition, expansion, technology development, research & development expansion and other market activities). Browse Full Report With TOC@ https://www.kdmarketinsights.com/product/business-intelligence-market-2017 Table of Content Research Methodology Market Definition and List of Abbreviations 1. Executive Summary 2. Growth Drivers & Issues in Global Business Intelligence (BI) Market 3. Global Business Intelligence (BI) Market Trends 4. Opportunities in Global Business Intelligence (BI) Market 5. Recent Industry Activities, 2017 6. Porter's Five Forces Analysis 7. Market Value Chain and Supply Chain Analysis 8. Products Average Price Analysis, By Country 9. Global Business Intelligence (BI) Market Value & Forecast (USD Million), 2017-2023 10. Global Business Intelligence (BI) Market Segmentation Analysis, By Component 10.1. Introduction 10.2. Market Attractiveness, By Component 10.3. BPS Analysis, By Component 10.4. Hardware Market Value & Forecast (USD Million), 2017-2023 10.5. Software Market Value & Forecast (USD Million), 2017-2023 10.6. Platform Market Value & Forecast (USD Million), 2017-2023 10.7. Services Market Value & Forecast (USD Million), 2017-2023 10.8. Managed Services Market Value & Forecast (USD Million), 2017-2023 10.9. Professional Services Market Value & Forecast (USD Million), 2017-2023 11. Global Business Intelligence (BI) Market Segmentation Analysis, By Data Type 11.1. Introduction 11.2. Market Attractiveness, By Data Type 11.3. BPS Analysis, By Data Type 11.4. Structured Data Market Value & Forecast (USD Million), 2017-2023 11.5. Semi-Structured Data Market Value & Forecast (USD Million), 2017-2023 11.6. Unstructured Data Market Value & Forecast (USD Million), 2017-2023 12. Global Business Intelligence (BI) Market Segmentation Analysis, By Technology 12.1. Introduction 12.2. Market Attractiveness, By Technology 12.3. BPS Analysis, By Technology 12.4. Cloud BI Market Value & Forecast (USD Million), 2017-2023 12.5. Traditional BI Market Value & Forecast (USD Million), 2017-2023 12.6. Social BI Market Value & Forecast (USD Million), 2017-2023 12.7. Mobile BI Market Value & Forecast (USD Million), 2017-2023 13. Global Business Intelligence (BI) Market Segmentation Analysis, By Deployment 13.1. Introduction 13.2. Market Attractiveness, By Deployment 13.3. BPS Analysis, By Deployment 13.4. On-Demand Market Value & Forecast (USD Million), 2017-2023 13.5. On-Premises Market Value & Forecast (USD Million), 2017-2023 14. Global Business Intelligence (BI) Market Segmentation Analysis, By Business Size 14.1. Introduction 14.2. Market Attractiveness, By Business Size 14.3. BPS Analysis, By Business Size 14.4. Small and Medium-Sized Enterprises Market Value & Forecast (USD Million), 2017-2023 14.5. Large Enterprises Market Value & Forecast (USD Million), 2017-2023 15. Global Business Intelligence (BI) Market Segmentation Analysis, By Application 15.1. Introduction 15.2. Market Attractiveness, By Application 15.3. BPS Analysis, By Application 15.4. Operations Management Market Value & Forecast (USD Million), 2017-2023 15.5. Network Management and Optimization Market Value & Forecast (USD Million), 2017-2023 15.6. Predictive Asset Maintenance Market Value & Forecast (USD Million), 2017-2023 15.7. Sales and Marketing Management Market Value & Forecast (USD Million), 2017-2023 15.8. Fraud Detection and Security Management Market Value & Forecast (USD Million), 2017-2023 15.9. Workforce Management Market Value & Forecast (USD Million), 2017-2023 15.10. Supply Chain Optimization Market Value & Forecast (USD Million), 2017-2023 15.11. Other Applications 16. Global Business Intelligence (BI) Market Segmentation Analysis, By End User 16.1. Introduction 16.2. Market Attractiveness, By End User 16.3. BPS Analysis, By End User 16.4. Media and Entertainment Market Value & Forecast (USD Million), 2017-2023 16.5. Manufacturing Market Value & Forecast (USD Million), 2017-2023 16.6. Telecommunications and IT Market Value & Forecast (USD Million), 2017-2023 16.7. Transportation and Logistics Market Value & Forecast (USD Million), 2017-2023 16.8. Retail and Consumer Goods Market Value & Forecast (USD Million), 2017-2023 16.9. Banking, Financial Services, and Insurance Market Value & Forecast (USD Million), 2017-2023 16.10. Energy and Utilities Market Value & Forecast (USD Million), 2017-2023 16.11. Healthcare and Life Sciences Market Value & Forecast (USD Million), 2017-2023 16.12. Government and Defense Market Value & Forecast (USD Million), 2017-2023 16.13. Other End Users Market Value & Forecast (USD Million), 2017-2023 17. Geographical Analysis 17.1. Introduction 17.2. North America Market Value & Forecast (USD Million), 2017-2023 17.2.1. By Component 17.2.1.1. Introduction 17.2.1.2. Market Attractiveness, By Component 17.2.1.3. BPS Analysis, By Component 17.2.1.4. Hardware Market Value & Forecast (USD Million), 2017-2023 17.2.1.5. Software Market Value & Forecast (USD Million), 2017-2023 17.2.1.6. Platform Market Value & Forecast (USD Million), 2017-2023 17.2.1.7. Services Market Value & Forecast (USD Million), 2017-2023 17.2.1.7.1. Managed Services Market Value & Forecast (USD Million), 2017-2023 17.2.1.7.2. Professional Services Market Value & Forecast (USD Million), 2017-2023 17.2.2. By Data Type 17.2.2.1. Introduction 17.2.2.2. Market Attractiveness, By Data Type 17.2.2.3. BPS Analysis, By Data Type 17.2.2.4. Structured Data Market Value & Forecast (USD Million), 2017-2023 17.2.2.5. Semi-Structured Data Market Value & Forecast (USD Million), 2017-2023 17.2.2.6. Unstructured Data Market Value & Forecast (USD Million), 2017-2023 17.2.3. By Technology 17.2.3.1. Introduction 17.2.3.2. Market Attractiveness, By Technology 17.2.3.3. BPS Analysis, By Technology 17.2.3.4. Cloud BI Market Value & Forecast (USD Million), 2017-2023 17.2.3.5. Traditional BI Market Value & Forecast (USD Million), 2017-2023 17.2.3.6. Social BI Market Value & Forecast (USD Million), 2017-2023 17.2.3.7. Mobile BI Market Value & Forecast (USD Million), 2017-2023 17.2.4. By Deployment 17.2.4.1. Introduction 17.2.4.2. Market Attractiveness, By Deployment 17.2.4.3. BPS Analysis, By Deployment 17.2.4.4. On-Demand Market Value & Forecast (USD Million), 2017-2023 17.2.4.5. On-Premises Market Value & Forecast (USD Million), 2017-2023 17.2.5. By Business Size 17.2.5.1. Introduction 17.2.5.2. Market Attractiveness, By Business Size 17.2.5.3. BPS Analysis, By Business Size 17.2.5.4. Small and Medium-Sized Enterprises Market Value & Forecast (USD Million), 2017-2023 17.2.5.5. Large Enterprises Market Value & Forecast (USD Million), 2017-2023 17.2.6. By Application 17.2.6.1. Introduction 17.2.6.2. Market Attractiveness, By Application 17.2.6.3. BPS Analysis, By Application 17.2.6.4. Operations Management Market Value & Forecast (USD Million), 2017-2023 17.2.6.5. Network Management and Optimization Market Value & Forecast (USD Million), 2017-2023 17.2.6.6. Predictive Asset Maintenance Market Value & Forecast (USD Million), 2017-2023 17.2.6.7. Sales and Marketing Management Market Value & Forecast (USD Million), 2017-2023 17.2.6.8. Fraud Detection and Security Management Market Value & Forecast (USD Million), 2017-2023 17.2.6.9. Workforce Management Market Value & Forecast (USD Million), 2017-2023 17.2.6.10. Supply Chain Optimization Market Value & Forecast (USD Million), 2017-2023 17.2.6.11. Other Applications 17.2.7. By End User 17.2.7.1. Introduction 17.2.7.2. Market Attractiveness, By End User 17.2.7.3. BPS Analysis, By End User 17.2.7.4. Media and Entertainment Market Value & Forecast (USD Million), 2017-2023 17.2.7.5. Manufacturing Market Value & Forecast (USD Million), 2017-2023 17.2.7.6. Telecommunications and IT Market Value & Forecast (USD Million), 2017-2023 17.2.7.7. Transportation and Logistics Market Value & Forecast (USD Million), 2017-2023 17.2.7.8. Retail and Consumer Goods Market Value & Forecast (USD Million), 2017-2023 17.2.7.9. Banking, Financial Services, and Insurance Market Value & Forecast (USD Million), 2017-2023 17.2.7.10. Energy and Utilities Market Value & Forecast (USD Million), 2017-2023 17.2.7.11. Healthcare and Life Sciences Market Value & Forecast (USD Million), 2017-2023 17.2.7.12. Government and Defense Market Value & Forecast (USD Million), 2017-2023 17.2.7.13. Other End Users Market Value & Forecast (USD Million), 2017-2023 17.2.8. By Country 17.2.8.1. Market Attractiveness, By Country 17.2.8.2. BPS Analysis, By Country 17.2.8.3. U.S. Market Value & Forecast (USD Million), 2017-2023 17.2.8.4. Canada Market Value & Forecast (USD Million), 2017-2023 Continue…. Check for Discount @ https://www.kdmarketinsights.com/discount/295 About Us: KD Market Insights offers a comprehensive database of syndicated research studies, customized reports, and consulting services. These reports are created to help in making smart, instant and crucial decisions based on extensive and in-depth quantitative information, supported by extensive analysis and industry insights. Our dedicated in-house team ensures the reports satisfy the requirement of the client. We aim at providing value service to our clients. Our reports are backed by extensive industry coverage and is made sure to give importance to the specific needs of our clients. The main idea is to enable our clients to make an informed decision, by keeping them and ourselves up to date with the latest trends in the market. Contact Us: KD Market Insights 150 State Street, Albany, New York, USA 12207 +1 (518) 300-1215 Email: sales@kdmarketinsights.com Website: www.kdmarketinsights.com

[amarmytectra-blog]

Informatica Interview Questions and Answers

Q1).What Is Informtica?

Ans: Informatica is a Software development company, which offers data integration products. If offers products for ETL, data masking, data Quality, data replica, data virtualization, master data management, etc.

Informatica Powercenter ETL/Data Integration tool is a most widely used tool and in the common term when we say Informatica, it refers to the Informatica PowerCenter tool for ETL.

Informatica Powercenter is used for Data integration. It offers the capability to connect & fetch data from different heterogeneous source and processing of data.

For example, you can connect to an SQL Server Database and Oracle Database both and can integrate the data into a third system.

The latest version of Informatica PowerCenter available is 9.6.0. The different editions for the PowerCenter are

Standard edition

Advanced edition

Premium edition

The popular clients using Informatica Powercenter as a data integration tool are U.S Air Force, Allianz, Fannie Mae, ING, Samsung, etc. The popular tools available in the market in competition to Informatica are IBM Datastage, Oracle OWB, Microsoft SSIS and Ab Initio.

OR

Define Informatica?

Ans: Informatica is a tool, supporting all the steps of Extraction, Transformation and Load process. Now days Informatica is also being used as an Integration tool.Informatica is an easy to use tool. It has got a simple visual interface like forms in visual basic. You just need to drag and drop different objects (known as transformations) and design process flow for Data extraction transformation and load.

These process flow diagrams are known as mappings. Once a mapping is made, it can be scheduled to run as and when required. In the background Informatica server takes care of fetching data from source, transforming it, & loading it to the target systems/databases.

Q2).Why do we need Informatica?

Ans: Informatica comes to the picture wherever we have a data system available and at the backend we want to perform certain operations on the data. It can be like cleaning up of data, modifying the data, etc. based on certain set of rules or simply loading of bulk data from one system to another.

Informatica offers a rich set of features like operations at row level on data, integration of data from multiple structured, semi-structured or unstructured systems, scheduling of data operation. It also has the feature of metadata, so the information about the process and data operations are also preserved.

Q3).What are the advantages of Informatica?

Ans: Informatica has some advantages over other data integration systems. A couple of the advantages are:

It is faster than the available platforms.

You can easily monitor your jobs with Informatica Workflow Monitor.

It has made data validation, iteration and project development to be easier than before.

If you experience failed jobs, it is easy to identify the failure and recover from it. The same applies to jobs that are running slowly.

Or

Its GUI tool, Coding in any graphical tool is generally faster than hand code scripting. Can communicate with all major data sources (mainframe/RDBMS/Flat Files/XML/VSM/SAP etc). Can handle vary large/huge data very effectively. User can apply Mappings, extract rules, cleansing rules, transformation rules, aggregation logic and loading rules are in separate objects in an ETL tool. Any change in any of the object will give minimum impact of other object. Reusability of the object (Transformation Rules) Informatica has different “adapters” for extracting data from packaged ERP applications (such as SAP or PeopleSoft). Availability of resource in the market. Can be run on Window and Unix environment.

Q4).In what real situations can Informatica be used?

Ans: Informatica has a wide range of application that covers areas such as:

Data migration.

Application migration.

Data warehousing.

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