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 Engineer 130

Data Engineer Data Engineering Data Engineering Data Engineering 130

IBM Journey to AI blog

APRIL 24, 2023

Data is reported from one central repository, enabling management to draw more meaningful business insights and make faster, better decisions. By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement.

Data Warehouse 112

Data Warehouse Data Lakes Artificial Intelligence Artificial Intelligence 112

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. The post How Will The Cloud Impact Data Warehousing Technologies?

Data Warehouse 141

Data Warehouse Big Data Big Data Business Intelligence 141

Analytics Vidhya

JUNE 22, 2022

This article was published as a part of the Data Science Blogathon. Introduction In today’s data-driven age, an enormous amount of data is getting generated every day from various sources such as social media, e-commerce websites, stock exchanges, transaction processing systems, emails, medical records, etc.

Data Science 369

Data Science Analytics Analytics Data Warehouse 369

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

Analytics Vidhya

JULY 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction Processing large amounts of raw data from various sources requires appropriate tools and solutions for effective data integration. Building an ETL pipeline using Apache […].

ETL 383

ETL Data Science Analytics Analytics 383

Smart Data Collective

OCTOBER 13, 2021

Multi-channel publishing of data services. Agile BI and Reporting, Single Customer View, Data Services, Web and Cloud Computing Integration are scenarios where Data Virtualization offers feasible and more efficient alternatives to traditional solutions. Does Data Virtualization support web data integration?

Data Visualization 141

Data Visualization Big Data Big Data Predictive Analytics 141

Towards AI

APRIL 10, 2024

Think of a VPC as a private space in the AWS cloud where we can place our resources, like EC2 instances, RDS, ECS (Elastic Container Service), EKS (Elastic Kubernetes Service), Redshift (Fully managed data warehouse service), and many more. This resource uses network configuration to communicate with others.

Database 122

Database Machine Learning Machine Learning AWS 122

Smart Data Collective

JANUARY 22, 2020

In recent years, cloud computing has gained increasing popularity and proved its effectiveness. There is no doubt that cloud services are changing the business environment. Small companies value the ability to store documents in the cloud and conveniently manage them. Risks Associated with Cloud Computing.

Cloud Computing 61

Cloud Computing Data Warehouse Database Algorithm 61

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

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

Analytics Vidhya

MARCH 31, 2023

Introduction Publish and Subscribe is a messaging mechanism having one or a set of senders sending messages and one or a group of receivers receiving these messages.

Analytics 328

Analytics Analytics Data Warehouse Data Engineer 328

Pickl AI

OCTOBER 6, 2024

It is a crucial data integration process that involves moving data from multiple sources into a destination system, typically a data warehouse. This process enables organisations to consolidate their data for analysis and reporting, facilitating better decision-making. ETL stands for Extract, Transform, and Load.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

Analytics Vidhya

FEBRUARY 17, 2023

Introduction Are you curious about the latest advancements in the data tech industry? Perhaps you’re hoping to advance your career or transition into this field. In that case, we invite you to check out DataHour, a series of webinars led by experts in the field.

Analytics 319

Analytics Analytics Data Pipeline Data Warehouse 319

Analytics Vidhya

APRIL 3, 2023

Introduction Companies can access a large pool of data in the modern business environment, and using this data in real-time may produce insightful results that can spur corporate success. Real-time dashboards such as GCP provide strong data visualization and actionable information for decision-makers.

Data Engineer 319

Data Engineer Data Engineering Data Engineering Data Engineering 319

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 Engineer Data Engineering 306

Analytics Vidhya

FEBRUARY 4, 2023

Introduction This article will explain the difference between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) when data transformation occurs. In ETL, data is extracted from multiple locations to meet the requirements of the target data file and then placed into the file.

ETL 298

ETL Analytics Analytics Data Warehouse 298

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 Engineer Data Engineering Data Engineering 271

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

Azure 283

Azure ETL Analytics Analytics 283

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

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 AI AI 72

Alation

DECEMBER 14, 2021

Breakout sessions shared cutting-edge use cases that hint at the future of cloud computing. These included: Johnson & Johnson is migrating its entire enterprise data warehouse to the cloud to get better performance, reduced costs, and superior scalability.

AWS 52

AWS Data Lakes Data Warehouse Machine Learning 52

AWS Machine Learning Blog

JULY 17, 2023

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon Redshift is a fully managed, fast, secure, and scalable cloud data warehouse.

Clustering 88

Clustering AWS ML ML 88

Alation

JULY 13, 2021

This recent cloud migration applies to all who use data. We have seen the COVID-19 pandemic accelerate the timetable of cloud data migration , as companies evolve from the traditional data warehouse to a data cloud, which can host a cloud computing environment.

Cloud Data 52

Cloud Data Data Governance Data Mining Data Mining 52

IBM Journey to AI blog

SEPTEMBER 26, 2023

States’ existing investments in modernizing and enhancing ancillary supportive technologies (such as document management, web portals, mobile applications, data warehouses and location services) could negate the need for certain system requirements as part of the child support system modernization initiative.

Data Warehouse 112

Data Warehouse Cloud Computing Cloud Data Analytics 112

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 Engineer Data Engineering 91

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

NOVEMBER 1, 2023

The Snowflake Data Cloud offers a scalable, cloud-native data warehouse that provides the flexibility, performance, and ease of use needed to meet the demands of modern businesses. Cloud computing resources can quickly become a financial burden if not managed effectively.

Data Warehouse 52

Data Warehouse SQL Database Cloud Computing 52

Analytics Vidhya

FEBRUARY 25, 2023

Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

Data Lakes 178

Data Lakes Analytics Analytics Data Warehouse 178

Precisely

MARCH 10, 2023

Think back to the early 2000s, a time of big data warehouses with rigid structures. Organizations searched for ways to add more data, more variety of data, bigger sets of data, and faster computing speed. There was a massive expansion of efforts to design and deploy big data technologies.

Data Warehouse 59

Data Warehouse Analytics Analytics Big Data 59

Precisely

JANUARY 25, 2024

Many organizations adopt a long-term approach, leveraging the relative strengths of both mainframe and cloud systems. This integrated strategy keeps a wide range of IT options open, blending the reliability of mainframes with the innovation of cloud computing.

Data Governance 69

Data Governance Database Cloud Data Data Lakes 69

phData

OCTOBER 12, 2023

Incremental processing and data freshness scans become trivial and easy thanks to the metadata Fivetran brings into your cloud data warehouse. Optimizing for Scale So what does it look like to actually optimize your pipelines to scale your data pipelines? These allow you to scale your pipelines quickly.

Data Warehouse 52

Data Warehouse Database Cloud Data Data Pipeline 52

IBM Journey to AI blog

JULY 31, 2023

Before the internet and cloud computing , and before smartphones and mobile apps, banks were shuttling payments through massive electronic settlement gateways and operating mainframes as systems of record. Complex analytical queries atop huge datasets on the mainframe can eat up compute budgets and take hours or days to run.

Analytics 64

Analytics Analytics Data Warehouse Cloud Computing 64

Pickl AI

APRIL 6, 2023

Common Interview Questions There are several Azure Data Engineer Interview questions that you may find if you appear for an interview in the field. Answer : Microsoft Azure is a cloud computing platform and service that Microsoft provides. Which service would you use to create Data Warehouse in Azure?

Azure 52

Azure Data Engineer Data Engineering Data Engineering 52

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