Data Science Blog

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

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Data Warehouse Azure SQL Database 259

Analytics Vidhya

JULY 18, 2022

This article was published as a part of the Data Science Blogathon. Introduction Organizations with a separate transactional database and data warehouse typically have many data engineering activities. For example, they extract, transform and load data from various sources into their data warehouse.

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ETL Data Warehouse Data Engineering Data Engineering 291

Analytics Vidhya

JANUARY 6, 2022

INTRODUCTION Hive is one of the most popular data warehouse systems in the industry for data storage, and to store this data Hive uses tables. Tables in the hive are analogous to tables in a relational database management system. By default, it is /user/hive/warehouse directory. For instance, […].

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Data Warehouse Database Analytics Analytics 364

Analytics Vidhya

FEBRUARY 27, 2023

Introduction Data is the new oil in this century. The database is the major element of a data science project. To generate actionable insights, the database must be centralized and organized efficiently. So, we are […] The post How to Normalize Relational Databases With SQL Code?

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Database SQL Data Science Analytics 319

AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Data Warehouse Machine Learning Machine Learning Cloud Data 112

Analytics Vidhya

JULY 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction We are all pretty much familiar with the common modern cloud data warehouse model, which essentially provides a platform comprising a data lake (based on a cloud storage account such as Azure Data Lake Storage Gen2) AND a data warehouse compute engine […].

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Azure Data Warehouse Data Lakes Analytics 398

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.

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Data Science Blog

MAY 20, 2024

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. It offers full BI-Stack Automation, from source to data warehouse through to frontend.

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Data Pipeline Data Warehouse Azure Data Lakes 262

Analytics Vidhya

SEPTEMBER 27, 2022

Businesses have adopted Snowflake as migration from on-premise enterprise data warehouses (such as Teradata) or a more flexibly scalable and easier-to-manage alternative to […]. The post Data Warehousing with Snowflake and Other Alternatives appeared first on Analytics Vidhya.

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Data Warehouse Data Science Analytics Analytics 397

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.

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Data Warehouse Hadoop Data Engineering Data Engineering 271

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.

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Data Warehouse Data Science Database Analytics 271

Analytics Vidhya

FEBRUARY 1, 2023

Introduction Nowadays, organizations are looking for multiple solutions to deal with big data and related challenges.

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Data Warehouse Big Data Big Data Analytics 305

Analytics Vidhya

DECEMBER 28, 2022

This article was published as a part of the Data Science Blogathon. Source: [link] Introduction If you are familiar with databases, or data warehouses, you have probably heard the term “ETL.” As the amount of data at organizations grow, making use of that data in analytics to derive business insights grows as well.

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ETL AWS Data Warehouse Data Science 215

Data Science Connect

JANUARY 27, 2023

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

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Data Engineering Data Engineering Data Engineering Data Engineer 130

Dataconomy

APRIL 27, 2023

What is an online transaction processing database (OLTP)? OLTP is the backbone of modern data processing, a critical component in managing large volumes of transactions quickly and efficiently. This approach allows businesses to efficiently manage large amounts of data and leverage it to their advantage in a highly competitive market.

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Database Data Scientist Data Mining Data Mining 159

Analytics Vidhya

MARCH 14, 2023

This results in the generation of so much data daily. This generated data is stored in the database and will maintain it. SQL is a structured query language used to read and write these databases. Introduction In today’s world, technology has increased tremendously, and many people are using the internet.

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SQL Database Analytics Analytics 271

Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. What is data engineering?

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Big Data Big Data Data Engineering Data Engineering 91

Data Science Blog

MAY 15, 2023

tl;dr Ein Data Lakehouse ist eine moderne Datenarchitektur, die die Vorteile eines Data Lake und eines Data Warehouse kombiniert. Organisationen können je nach ihren spezifischen Bedürfnissen und Anforderungen zwischen einem Data Warehouse und einem Data Lakehouse wählen.

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Data Warehouse Data Lakes Azure AWS 147

IBM Data Science in Practice

JANUARY 13, 2025

The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines. Background One of the Analytics teams tasks is to load data from multiple sources and unify it into a data warehouse. Database size limits of 10GB.

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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IBM Journey to AI blog

MARCH 14, 2024

The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage. Also, traditional database management tasks, including backups, upgrades and routine maintenance drain valuable time and resources, hindering innovation.

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Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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Pickl AI

AUGUST 1, 2023

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

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Data Engineering Data Engineering Data Engineering Data Engineer 52

Flipboard

DECEMBER 11, 2024

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Complete the following steps: On the project page, choose Data.

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ODSC - Open Data Science

JANUARY 30, 2024

Data engineering is a hot topic in the AI industry right now. And as data’s complexity and volume grow, its importance across industries will only become more noticeable. But what exactly do data engineers do? So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest.

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Data Engineering Data Engineering Data Engineering Data Engineer 52

Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

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Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

NOVEMBER 8, 2024

Data Versioning and Time Travel Open Table Formats empower users with time travel capabilities, allowing them to access previous dataset versions. Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data.

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Data Lakes Data Warehouse Database Azure 52

phData

MARCH 8, 2023

Fivetran is used by businesses to centralize data from various sources into a single, comprehensive data warehouse. It allows organizations to easily connect their disparate data sources without having to manage any infrastructure. This frees up our data engineers to do what they do best.

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

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Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

APRIL 25, 2023

We also made the case that query and reporting, provided by big data engines such as Presto, need to work with the Spark infrastructure framework to support advanced analytics and complex enterprise data decision-making. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

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Data Engineering Data Engineering Data Engineering Data Engineer 52

ODSC - Open Data Science

FEBRUARY 15, 2024

We couldn’t be more excited to announce the first sessions for our second annual Data Engineering Summit , co-located with ODSC East this April. Join us for 2 days of talks and panels from leading experts and data engineering pioneers. Is Gen AI A Data Engineering or Software Engineering Problem?

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Data Engineering Data Engineering Data Engineering Data Engineer 52

AWS Machine Learning Blog

DECEMBER 29, 2022

Many of the RStudio on SageMaker users are also users of Amazon Redshift , a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

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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 […].

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ETL Data Science Analytics Analytics 383

Analytics Vidhya

FEBRUARY 28, 2023

Introduction One of the most important assets of any organization is the data it produces on a daily basis. This data is used by an organization to find valuable insights which help in improving an organization’s growth and strategies and give them an upper hand over its competitors.

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Analytics Analytics Data Warehouse Data Engineering 326

Analytics Vidhya

JULY 20, 2022

This article was published as a part of the Data Science Blogathon. Introduction Data acclimates to countless shapes and sizes to complete its journey from a source to a destination. The post Developing an End-to-End Automated Data Pipeline appeared first on Analytics Vidhya.

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Data Pipeline ETL Data Science Analytics 361

Pickl AI

JULY 14, 2024

It highlights their unique functionalities and applications, emphasising their roles in maintaining data integrity and facilitating efficient data retrieval in database design and management. Understanding the significance of different types of keys in DBMS is crucial for effective database design and management.

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