Analytics Vidhya

DECEMBER 31, 2022

This article was published as a part of the Data Science Blogathon. Introduction Data sharing has become so easy today, and we can share the details with just a few clicks. The post How to Encrypt and Decrypt the Data in PySpark? These details can get leaked if the […].

Data Science 317

Data Science Analytics Analytics Data Warehouse 317

Analytics Vidhya

DECEMBER 26, 2022

This article was published as a part of the Data Science Blogathon. Overview ETL (Extract, Transform, and Load) is a very common technique in data engineering. It involves extracting the operational data from various sources, transforming it into a format suitable for business needs, and loading it into data storage systems.

ETL 306

ETL AWS Data Engineering Data Engineering 306

Analytics Vidhya

FEBRUARY 6, 2023

Introduction The demand for data to feed machine learning models, data science research, and time-sensitive insights is higher than ever thus, processing the data becomes complex. To make these processes efficient, data pipelines are necessary.

Data Pipeline 271

Data Pipeline Data Engineering Data Engineering Data Engineer 271

Analytics Vidhya

DECEMBER 23, 2022

This article was published as a part of the Data Science Blogathon. Source: [link] Introduction In today’s digital world, data is generated at a swift pace. Data in itself is not useful unless we present it in a meaningful way and derive insights that help in making key business decisions.

Business Intelligence 271

Business Intelligence Business Intelligence Data Science Analytics 271

Data Science Connect

JANUARY 27, 2023

Understand data warehousing concepts: Data warehousing is the process of collecting, storing, and managing large amounts of data. Understanding how data warehousing works and how to design and implement a data warehouse is an important skill for a data engineer.

Data Engineering 130

Data Engineering Data Engineering Data Engineer Data Engineering 130

Smart Data Collective

APRIL 8, 2020

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. Big data and data warehousing.

Data Warehouse 144

Data Warehouse Big Data Big Data Big Data Analytics 144

IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

ODSC - Open Data Science

JUNE 22, 2023

These are called data lakes. What Are Data Lakes? Unlike databases and data warehouses, data lakes can store data in raw and unstructured forms. This feature is important because it allows data lakes to hold a larger amount of data and store it faster.

Data Lakes 98

Data Lakes Data Warehouse Database Data Science 98

Dataconomy

SEPTEMBER 27, 2023

The Microsoft Certified Solutions Associate and Microsoft Certified Solutions Expert certifications cover a wide range of topics related to Microsoft’s technology suite, including Windows operating systems, Azure cloud computing, Office productivity software, Visual Studio programming tools, and SQL Server databases.

Database Administration 113

Database Administration Database Azure Cloud Computing 113

Dataconomy

FEBRUARY 12, 2024

Businesses increasingly rely on up-to-the-moment information to respond swiftly to market shifts and consumer behaviors Unstructured data challenges : The surge in unstructured data—videos, images, social media interactions—poses a significant challenge to traditional ETL tools.

ETL 195

ETL Data Governance Machine Learning Machine Learning 195

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. Data ingestion/integration services. Data orchestration tools.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Mlearning.ai

FEBRUARY 16, 2023

The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

IBM Journey to AI blog

DECEMBER 7, 2023

Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses. They are seamlessly integrated with cloud-based data warehouses, facilitating the collection, storage and analysis of data from various sources.

Data Preparation 72

Data Preparation Database Data Analysis Data Analysis 72

Pickl AI

SEPTEMBER 5, 2024

Summary: This blog provides a comprehensive roadmap for aspiring Azure Data Scientists, outlining the essential skills, certifications, and steps to build a successful career in Data Science using Microsoft Azure. Microsoft Azure, often referred to as Azure, is a robust cloud computing platform developed by Microsoft.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

ODSC - Open Data Science

JULY 11, 2023

Hybrid data centers: This refers to a combination of different data center solutions such as using a mix of on-premises, co-location, and cloud-based data centers to meet specific needs. Alternatives to using a data center: 1. They are typically used by organizations to store and manage their own data.

Data Lakes 40

Data Lakes AI AI Cloud Computing 40

Alation

APRIL 14, 2022

Cloud providers like Amazon Web Services, Microsoft Azure, Google, and Alibaba not only provide capacity beyond what the data center can provide, their current and emerging capabilities and services drive the execution of AI/ML away from the data center. The future lies in the cloud. Scheduling. Target Matching.

Data Governance 59

Data Governance ML ML Cloud Data 59

Pickl AI

DECEMBER 10, 2024

Purpose Exalogic serves as a platform for hosting high-performance cloud computing and enterprise applications. It ensures that businesses can process large volumes of data quickly, efficiently, and reliably. The Hybrid Columnar Compression feature dramatically reduces storage requirements by compressing data intelligently.

Database 52

Database Analytics Analytics Business Intelligence 52