Analytics Vidhya

OCTOBER 28, 2021

This article was published as a part of the Data Science Blogathon What is the need for Hive? The official description of Hive is- ‘Apache Hive data warehouse software project built on top of Apache Hadoop for providing data query and analysis.

Apache Hadoop 349

Apache Hadoop Data Warehouse Hadoop SQL 349

Analytics Vidhya

AUGUST 17, 2022

This article was published as a part of the Data Science Blogathon. Introduction Impala is an open-source and native analytics database for Hadoop. Vendors such as Cloudera, Oracle, MapReduce, and Amazon have shipped Impala. If you want to learn all things Impala, you’ve come to the right place.

Hadoop 292

Hadoop Data Science Database Analytics 292

Analytics Vidhya

JULY 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction One of the sources of Big Data is the traditional application management system or the interaction of applications with relational databases using RDBMS. Big Data storage and analysis […].

Big Data 399

Big Data Big Data Database Data Science 399

Analytics Vidhya

JUNE 30, 2022

This article was published as a part of the Data Science Blogathon. Introduction Hive is a popular data warehouse built on top of Hadoop that is used by companies like Walmart, Tiktok, and AT&T. It is an important technology for data engineers to learn and master.

Data Warehouse 271

Data Warehouse Hadoop Data Engineering Data Engineering 271

Data Science Dojo

JULY 3, 2024

Data science bootcamps are intensive short-term educational programs designed to equip individuals with the skills needed to enter or advance in the field of data science. They cover a wide range of topics, ranging from Python, R, and statistics to machine learning and data visualization.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Data Science Dojo

JULY 12, 2023

Welcome to the world of databases, where the choice between SQL (Structured Query Language) and NoSQL (Not Only SQL) databases can be a significant decision. In this blog, we’ll explore the defining traits, benefits, use cases, and key factors to consider when choosing between SQL and NoSQL databases.

SQL 195

SQL Database Big Data Big Data 195

Analytics Vidhya

APRIL 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction Have you ever wondered how big IT giants store and process huge amounts of data? storing the data […].

Data Warehouse 267

Data Warehouse Database Data Science Analytics 267

Pickl AI

NOVEMBER 4, 2024

Summary: Python for Data Science is crucial for efficiently analysing large datasets. Introduction Python for Data Science has emerged as a pivotal tool in the data-driven world. Key Takeaways Python’s simplicity makes it ideal for Data Analysis. in 2022, according to the PYPL Index.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Analytics Vidhya

SEPTEMBER 18, 2022

This article was published as a part of the Data Science Blogathon. Introduction Apache SQOOP is a tool designed to aid in the large-scale export and import of data into HDFS from structured data repositories. Relational databases, enterprise data warehouses, and NoSQL systems are all examples of data storage.

Data Warehouse 271

Data Warehouse Data Science Database Analytics 271

Becoming Human

JANUARY 23, 2023

Data Science You heard this term most of the time all over the internet, as well this is the most concerning topic for newbies who want to enter the world of data but don’t know the actual meaning of it. I’m not saying those are incorrect or wrong even though every article has its mindset behind the term ‘ Data Science ’.

Data Science 105

Data Science Big Data Big Data Deep Learning 105

Data Science 101

JUNE 10, 2019

Getting your first data science job might be challenging, but it’s possible to achieve this goal with the right resources. Before jumping into a data science career , there are a few questions you should be able to answer: How do you break into the profession? What skills do you need to become a data scientist?

Data Science 96

Data Science Data Scientist Data Analyst Data Engineering 96

Data Science Dojo

JANUARY 12, 2023

It can process any type of data, regardless of its variety or magnitude, and save it in its original format. Hadoop systems and data lakes are frequently mentioned together. However, instead of using Hadoop, data lakes are increasingly being constructed using cloud object storage services.

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

Analytics Vidhya

APRIL 20, 2022

This article was published as a part of the Data Science Blogathon. Introduction Modern applications and products deal with large amounts of data. The quantity of data being processed and utilised in modern times is enormous. How to manage large files and data. So, the question arises?

Data Science 307

Data Science Analytics Analytics Hadoop 307

IBM Journey to AI blog

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Analytics Vidhya

MAY 5, 2022

This article was published as a part of the Data Science Blogathon. Introduction With a huge increment in data velocity, value, and veracity, the volume of data is growing exponentially with time. This outgrows the storage limit and enhances the demand for storing the data across a network of machines.

Data Science 270

Data Science Analytics Analytics Apache Hadoop 270

Dataconomy

JULY 25, 2023

Is data science a good career? So, if a simple yes has convinced you, you can go straight to learning how to become a data scientist. But if you want to learn more about data science, today’s emerging profession that will shape your future, just a few minutes of reading can answer all your questions.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

Data Science Dojo

MAY 24, 2024

AI engineering is the discipline that combines the principles of data science, software engineering, and machine learning to build and manage robust AI systems. R provides excellent packages for data visualization, statistical testing, and modeling that are integral for analyzing complex datasets in AI. What is AI Engineering?

Machine Learning 195

Machine Learning Machine Learning Deep Learning Deep Learning 195

Smart Data Collective

OCTOBER 14, 2019

They’re looking to hire experienced data analysts, data scientists and data engineers. With big data careers in high demand, the required skillsets will include: Apache Hadoop. Software businesses are using Hadoop clusters on a more regular basis now. NoSQL and SQL. Machine Learning. Apache Spark.

Big Data 99

Big Data Big Data Apache Hadoop Hadoop 99

Smart Data Collective

FEBRUARY 21, 2020

Maintaining product databases. They need to know how to use big data to handle these responsibilities. On top of that, you will need to possess different skills, including advanced mathematics, time management and data science to ensure a great work ethic. Database Design Electronic System Management.

Big Data 142

Big Data Big Data Data Mining Data Mining 142

Smart Data Collective

APRIL 8, 2020

Data warehouse, also known as a decision support database, refers to a central repository, which holds information derived from one or more data sources, such as transactional systems and relational databases. The data collected in the system may in the form of unstructured, semi-structured, or structured data.

Data Warehouse 144

Data Warehouse Big Data Big Data Big Data Analytics 144

Dataconomy

JULY 24, 2023

If you’ve found yourself asking, “How to become a data scientist?” In this detailed guide, we’re going to navigate the exciting realm of data science, a field that blends statistics, technology, and strategic thinking into a powerhouse of innovation and insights. ” you’re in the right place.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

IBM Data Science in Practice

OCTOBER 25, 2024

To know more about IBM SPSS Analytic Server [link] IBM SPSS ANALYTIC SERVER enables IBM SPSS Modeler to use big data as a source for predictive modelling. Together they can provide an integrated predictive analytics platform, using data from Hadoop distributions and Spark applications.

Analytics 100

Analytics Analytics Database Predictive Analytics 100

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Smart Data Collective

NOVEMBER 8, 2020

Big data is changing the future of almost every industry. The market for big data is expected to reach $23.5 Data science is an increasingly attractive career path for many people. If you want to become a data scientist, then you should start by looking at the career options available. Understand the Databases.

Data Engineering 104

Data Engineering Data Engineering Data Engineering Data Engineer 104

Pickl AI

JULY 18, 2023

Data Science helps businesses uncover valuable insights and make informed decisions. Programming for Data Science enables Data Scientists to analyze vast amounts of data and extract meaningful information. 8 Most Used Programming Languages for Data Science 1.

Data Science 52

Data Science SQL Data Scientist Python 52

Pickl AI

JUNE 19, 2024

Summary: Confused about Data Science course requirements? Learn how to assess courses and prepare for enrollment to launch your Data Science journey. The world runs on data. From targeted advertising to personalized healthcare, Data Science is revolutionizing every industry. Let’s Get Started !!!

Data Science 52

Data Science Data Scientist Hypothesis Testing Natural Language Processing 52

Pickl AI

DECEMBER 4, 2024

Summary: The future of Data Science is shaped by emerging trends such as advanced AI and Machine Learning, augmented analytics, and automated processes. As industries increasingly rely on data-driven insights, ethical considerations regarding data privacy and bias mitigation will become paramount.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

APRIL 2, 2024

Summary This blog post demystifies data science for business leaders. It explains key concepts, explores applications for business growth, and outlines steps to prepare your organization for data-driven success. Data Science Cheat Sheet for Business Leaders In today’s data-driven world, information is power.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. This ensures data consistency and integrity.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Data Science Dojo

JULY 24, 2023

Big Data Engineering Understanding big data engineering Big data and its characteristics (Volume, Velocity, Variety, Veracity) Big Data refers to the enormous volume of data that is generated at a high velocity from diverse sources, including structured and unstructured data. text, images, videos).

Big Data 195

Big Data Big Data Data Engineering Data Engineering 195

Pickl AI

OCTOBER 22, 2024

Summary: Relational database organize data into structured tables, enabling efficient retrieval and manipulation. They ensure data integrity and reduce redundancy through defined relationships. Introduction What if you could instantly access any piece of information you need, without having to sift through piles of data?

Database 52

Database SQL Big Data Big Data 52

Mlearning.ai

MARCH 15, 2023

The roles of data scientists and data analysts cannot be over-emphasized as they are needed to support decision-making. This article will serve as an ultimate guide to choosing between Data Science and Data Analytics. Before going into the main purpose of this article, what is data?

Data Science 52

Data Science Analytics Analytics Data Analyst 52

Pickl AI

OCTOBER 14, 2024

Summary: A Masters in Data Science in India prepares students for exciting careers in a growing field. Introduction In today’s data-driven world, Data Science is crucial across industries, transforming raw data into actionable insights. Why Pursue a Master’s in Data Science?

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Data Science Connect

JANUARY 27, 2023

Learn SQL: As a data engineer, you will be working with large amounts of data, and SQL is the most commonly used language for interacting with databases. Understanding how to write efficient and effective SQL queries is essential.

Data Engineering 130

Data Engineering Data Engineering Data Engineering Data Engineer 130

ODSC - Open Data Science

MAY 31, 2023

It is typically a single store of all enterprise data, including raw copies of source system data and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning. Yes, many people still need a data lake (for their relevant data, not all enterprise data).

Data Lakes 131

Data Lakes Machine Learning Machine Learning Apache Kafka 131

Smart Data Collective

SEPTEMBER 23, 2020

Type of Data: structured and unstructured from different sources of data Purpose: Cost-efficient big data storage Users: Engineers and scientists Tasks: storing data as well as big data analytics, such as real-time analytics and deep learning Sizes: Store data which might be utilized. Data Warehouse.

Data Lakes 135

Data Lakes Data Warehouse Big Data Big Data 135

DagsHub

DECEMBER 11, 2023

The following points illustrates some of the main reasons why data versioning is crucial to the success of any data science and machine learning project: Storage space One of the reasons of versioning data is to be able to keep track of multiple versions of the same data which obviously need to be stored as well.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98