ProjectPro

JUNE 6, 2025

Data is often referred to as the new oil, and just like oil requires refining to become useful fuel, data also needs a similar transformation to unlock its true value. This transformation is where data warehousing tools come into play, acting as the refining process for your data. Why Choose a Data Warehousing Tool?

Data Storage 58

Data Storage PostgreSQL Data Warehouse AWS 58

ProjectPro

JUNE 6, 2025

A data architect role involves working with dataflow management and data storage strategies to create a sustainable database management system for an organization. Types of Data Architect Careers Data architects can apply their skills in several ways and in various job roles. Understanding of Data modeling tools (e.g.,

Data Architect 40

Data Architect Data Mining Programming Language Java 40

ProjectPro

JUNE 6, 2025

Million opportunities for remote and on-site data engineering roles. So, have you been wondering what happens to all the data collected from different sources, logs on your machine, data generated from your mobile, data in databases, customer data, and so on? We call this system Data Engineering.

Data Science 40

Data Science Data Engineering Data Engineer Engineering 40

ProjectPro

JUNE 6, 2025

However, the vast volume of data will overwhelm you if you start looking at historical trends. The time-consuming method of data collection and transformation can be eliminated using ETL. You can analyze and optimize your investment strategy using high-quality structured data.

BI 45

BI ETL Tools Retail Healthcare 45

ProjectPro

JUNE 6, 2025

This improves efficiency and reduces the need for extensive post-processing or manual intervention, making the use of LLMs essential for industries that rely on high-quality data from web sources. Role of LLMs for Web Scraping LLMs are adept at handling unstructured data and transforming it into meaningful insights.

Project 40

Project Unstructured Data Raw Data Python 40

AltexSoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

JUNE 6, 2025

Characteristics of a Data Science Pipeline Data Science Pipeline Workflow Data Science Pipeline Architecture Building a Data Science Pipeline - Steps Data Science Pipeline Tools 5 Must-Try Projects on Building a Data Science Pipeline Master Building Data Pipelines with ProjectPro!

Data Science 40

Data Science Building Data Lake AWS 40

ProjectPro

JUNE 6, 2025

Big data analytics market is expected to be worth $103 billion by 2023. We know that 95% of companies cite managing unstructured data as a business problem. of companies plan to invest in big data and AI. million managers and data analysts with deep knowledge and experience in big data. While 97.2%

Big Data 45

Big Data Big Data Skills Scala Hadoop 45

ProjectPro

JUNE 6, 2025

Data preparation for machine learning algorithms is usually the first step in any data science project. It involves various steps like data collection, data quality check, data exploration, data merging, etc. This blog covers all the steps to master data preparation with machine learning datasets.

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Data Preparation Machine Learning Project IT 45

ProjectPro

JUNE 6, 2025

The reason for this growing importance is simple: the world is becoming increasingly data-driven. Learning basic AI concepts , particularly in the beginner-friendly domain of data analysis , will thus become a must-have skill among professionals of different industries. FAQs What is Artificial Intelligence for Data Analysis?

Data Analytics 40

Data Analytics Healthcare Machine Learning BI 40

ProjectPro

JUNE 6, 2025

The Azure Data Factory ETL pipeline will involve extracting data from multiple manufacturing systems, transforming it into a format suitable for analysis, and loading it into a centralized data warehouse. The pipeline will handle data from various sources, including structured and unstructured data in different formats.

Coding 40

Coding Manufacturing Data Cleanse Data Warehouse 40

ProjectPro

APRIL 17, 2015

This articles explores four latest trends in big data analytics that are driving implementation of cutting edge technologies like Hadoop and NoSQL. The big data analytics market in 2015 will revolve around the Internet of Things (IoT), Social media sentiment analysis, increase in sensor driven wearables, etc.

Big Data 52

Big Data Data Analysis NoSQL Deep Learning 52

ProjectPro

JUNE 6, 2025

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Image Credit: wired.com The rate at which we are generating data is frightening - leading to “ Datafication ” of the world. Big data analysis has become a common practice in politics.

Big Data 40

Big Data Retail Banking Pharmaceutical 40

ProjectPro

JUNE 6, 2025

Table of Contents What is Real-Time Data Ingestion? Let us understand the key steps involved in real-time data ingestion into HDFS using Sqoop with the help of a real-world use case where a retail company collects real-time customer purchase data from point-of-sale systems and e-commerce platforms.

Data Ingestion 40

Data Ingestion Kafka Google Cloud AWS 40

ProjectPro

JUNE 6, 2025

Data Engineer Interview Questions on Big Data Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JUNE 6, 2025

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data 40

Big Data Hadoop Relational Database NoSQL 40

ProjectPro

JUNE 6, 2025

1) Build an Uber Data Analytics Dashboard This data engineering project idea revolves around analyzing Uber ride data to visualize trends and generate actionable insights. Reddit, being a vast community-driven platform, provides a rich data source for extracting valuable insights.

Data Engineering 59

Data Engineering Data Engineer Project Engineering 59

ProjectPro

JUNE 6, 2025

Automated tools are developed as part of the Big Data technology to handle the massive volumes of varied data sets. Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. Your organization will use internal and external sources to port the data.

Big Data 40

Big Data Data Engineering Data Engineer Engineering 40

ProjectPro

MAY 23, 2015

Use market basket analysis to classify shopping trips Walmart Data Analyst Interview Questions Walmart Hadoop Interview Questions Walmart Data Scientist Interview Question American multinational retail giant Walmart collects 2.5 petabytes of unstructured data from 1 million customers every hour. Inkiru Inc.

Big Data 52

Big Data Data Analysis Retail Hadoop 52

ProjectPro

JUNE 6, 2025

To develop the predictive model, data science experts or analysts generate standard predictive algorithms and statistical models, train them using subsets of the data, and execute them against the entire data set. Data Mining- You cleanse your data sets through data mining or data cleaning.

Data Mining 45

Data Mining Banking Retail Healthcare 45

ProjectPro

APRIL 14, 2015

Big data dating is the secret of success behind long lasting romance in relationships of the 21 st century. This article elaborates how online dating data is used by companies to help customers find the secret to long lasting romance through data analysis techniques. billion by 2016. Image Credit: linkurio.us

Big Data 52

Big Data Data Analysis MongoDB Hadoop 52

ProjectPro

JUNE 6, 2025

An AWS Data Scientist is a professional who combines expertise in data analysis, machine learning , and AWS technologies to extract meaningful insights from vast datasets. They are responsible for designing and implementing scalable, cost-effective AWS solutions, ensuring organizations can make data-driven decisions.

AWS 40

AWS Amazon Web Services Cloud Computing Machine Learning 40

Knowledge Hut

JANUARY 18, 2024

Data Science is a field of study that handles large volumes of data using technological and modern techniques. This field uses several scientific procedures to understand structured, semi-structured, and unstructured data. Both data science and software engineering rely largely on programming skills.

Software Engineering 97

Software Engineering Software Engineer Data Science Engineering 97

ProjectPro

JUNE 6, 2025

FAQs What is Synthetic Data Generation? Synthetic data generation is a technique used to create artificial data that mimics the characteristics and structure of real-world data. Scalability As organizations scale their operations, the need for large volumes of data grows.

Healthcare 40

Healthcare Medical Datasets Machine Learning 40

ProjectPro

JUNE 6, 2025

From automating report generation and data analysis to tackling complex challenges like fraud detection and risk assessment, generative AI is revolutionizing operations. For example, Workiva uses AI to detect and flag anomalies in financial data, such as duplicate payments or unusual expense entries, preventing errors.

Finance 52

Finance Banking Portfolio Datasets 52

ProjectPro

JUNE 6, 2025

FAQs How to Start an AI Project: The Prerequisites Implementing AI systems requires a solid understanding of its various subsets, such as Data Analysis , Machine Learning (ML) , Deep Learning (DL) , and Natural Language Processing (NLP). AI models rely on vast amounts of quality data to make accurate predictions.

Project 40

Project Deep Learning Datasets Machine Learning 40

ProjectPro

JUNE 6, 2025

The term "raw data" refers to a group of data (texts, photos, and database records in their raw form) that has not yet been fully processed and integrated into the system. Why is Data Wrangling important in Data Science Projects? Data wrangling is integral to any data science or data analytics project.

Raw Data 58

Raw Data Programming Language Unstructured Data Google Cloud 58

ProjectPro

JUNE 6, 2025

and various Apache technologies, enable real-time processing and analysis of large datasets, ensuring timely insights and actionable intelligence. Machine Learning (ML) Machine learning algorithms play a central role in AIOps, allowing platforms to adapt and evolve automatically based on data analysis.

Machine Learning 40

Machine Learning Algorithm Big Data Aggregated Data 40

ProjectPro

JUNE 6, 2025

From working with raw data in various formats to the complex processes of transforming and loading data into a central repository and conducting in-depth data analysis using SQL and advanced techniques, you will explore a wide range of real-world databases and tools.

Data Engineering 45

Data Engineering Data Engineer Cloud Engineering 45

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. However, the abundance of data opens numerous possibilities for research and analysis.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

AltexSoft

MAY 12, 2022

Audio data file formats. Similar to texts and images, audio is unstructured data meaning that it’s not arranged in tables with connected rows and columns. Audio data transformation basics to know. It’s worth noting that audio analysis involves working with images rather than listening. Audio data analysis steps.

Machine Learning 122

Machine Learning Building Deep Learning Healthcare 122

ProjectPro

JUNE 6, 2025

Additionally, Spark provides a wide range of high-level tools, such as Spark Streaming , MLlib for machine learning, GraphX for processing graph data sets, and Spark SQL for real-time processing of structured and unstructured data. Real-time data collection from Twitter is done with Spark Streaming.

Big Data Tools 40

Big Data Tools Big Data Hadoop Kafka 40

Knowledge Hut

DECEMBER 26, 2023

Of course, handling such huge amounts of data and using them to extract data-driven insights for any business is not an easy task; and this is where Data Science comes into the picture. To make accurate conclusions based on the analysis of the data, you need to understand what that data represents in the first place.

Data Science 97

Data Science BI Data Mining Business Intelligence 97

Knowledge Hut

DECEMBER 26, 2023

However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured. This mainly happened because data that is collected in recent times is vast and the source of collection of such data is varied, for example, data collected from text files, financial documents, multimedia data, sensors, etc.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

AltexSoft

FEBRUARY 11, 2023

The former uses data to generate insights and help businesses make better decisions, while the latter designs data frameworks, flows, standards, and policies that facilitate effective data analysis. But first, all candidates must be accredited by Arcitura as Big Data professionals.

Data Architect 87

Data Architect Certification Generalist Metadata 87

Knowledge Hut

APRIL 25, 2024

Big data vs machine learning is indispensable, and it is crucial to effectively discern their dissimilarities to harness their potential. Big Data vs Machine Learning Big data and machine learning serve distinct purposes in the realm of data analysis.

Machine Learning 52

Machine Learning Big Data Unstructured Data Data Mining 52

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. In this role, they would help the Analytics team become ready to leverage both structured and unstructured data in their model creation processes. They construct pipelines to collect and transform data from many sources.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98