Cloud Academy

JANUARY 27, 2022

Data engineering is the process of designing and implementing solutions to collect, store, and analyze large amounts of data. The data then gets prepared in formats to be used by people such as business analysts, data analysts, and data scientists. What does a data engineer do?

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Certification Data Engineering Data Engineer Engineering 52

Christophe Blefari

MARCH 1, 2023

dbt Core is an open-source framework that helps you organise data warehouse SQL transformation. dbt was born out of the analysis that more and more companies were switching from on-premise Hadoop data infrastructure to cloud data warehouses. This switch has been lead by modern data stack vision.

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ProjectPro

JANUARY 19, 2022

Data Engineering is gradually becoming a popular career option for young enthusiasts. Explore this page further and learn everything about data engineers to find the answer. We will cover it all, from its definition, skills, responsibilities to the significance of data engineer in an institution.

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Data Engineering Data Engineer Engineering Amazon Web Services 52

Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

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Data Ingestion Data Engineering Data Engineer Engineering 52

ProjectPro

JUNE 9, 2021

This blog on Data Science vs. Data Engineering presents a detailed comparison between the two domains. vs. What does a Data Engineer do? Are you a Data Scientist or a Data Engineer? Is data engineering more important than data science?

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Data Engineering Data Engineer Engineering Amazon Web Services 52

Towards Data Science

FEBRUARY 6, 2024

The disconnection between the operational teams immersed in the day-to-day functions and those extracting business value from data generated in the operational processes still remains a significant friction point. Searching for data Imagine being a data engineer/analyst tasked with identifying the top-selling products within your company.

Systems 83

Systems Raw Data Metadata Data Cleanse 83

Knowledge Hut

MARCH 7, 2024

The greatest data processing challenge of 2024 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data. Inability to process large volumes of data Out of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it.

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Big Data Bytes Data Governance Raw Data 98

Christophe Blefari

APRIL 21, 2023

A lot of data teams embraced dbt, or at least the SQL with engineering practices to transform data in cloud data warehouses. The content of the post is more about the future and the vision of the next big thing in analytics engineering: new models capabilities. In dbt Core 1.5

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Raw Data Data Datasets SQL 130

Data Engineering Podcast

DECEMBER 11, 2021

In this episode he explains how it can be used to build rich and useful profiles of your users, the system architecture that powers it, and some of the ways that it is being integrated into an organization’s broader data ecosystem. Missing data? Start trusting your data with Monte Carlo today! Stale dashboards?

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Data Warehouse Raw Data Data Lake BI 100

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. Read More: What is ETL?

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Data Lake ETL Tools Data Warehouse Data Pipeline 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Hevo

MARCH 31, 2023

dbt is a data transformation tool used by data engineers to process raw data within data warehouses. While dbt is a robust tool for transformation, you should be aware of the dbt commands to harness the power of dbt. Mastering dbt commands will let you run complex asks on dbt projects.

Raw Data 52

Raw Data Data Warehouse Data Engineering Data Engineer 52

DataKitchen

AUGUST 19, 2021

Each data source is updated on its own schedule, for example, daily, weekly or monthly. The DataKitchen Platform ingests data into a data lake and runs Recipes to create a data warehouse leveraged by users and self-service data analysts. Pharma Data Mesh Domains. The new Recipes run, and BOOM!

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Pharmaceutical Data Lake Data Warehouse Raw Data 98

Workfall

SEPTEMBER 18, 2023

Meet Airbyte, the data magician that turns integration complexities into child’s play. In this digital era, businesses thrive on data, and making this data dance harmoniously with your analytics tools is crucial. Airbyte ensures that you don’t miss out on those insights due to tangled data integration processes.

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Data Pipeline Raw Data Data Schemas Healthcare 93

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

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Data Warehouse SQL PostgreSQL Database 69

Data Engineering Podcast

DECEMBER 16, 2019

Even if you already have a metadata repository this is worth a listen to learn more about the value that visibility of your data can bring to your organization. You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse?

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Metadata PostgreSQL Datasets Data Warehouse 100

ProjectPro

FEBRUARY 16, 2023

ETL is a critical component of success for most data engineering teams, and with teams harnessing it with the power of AWS, the stakes are higher than ever. Data Engineers and Data Scientists require efficient methods for managing large databases, which is why centralized data warehouses are in high demand.

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AWS Data Management ETL Tools Management 52

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Data warehouse vs. data lake in a nutshell.

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Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

JANUARY 29, 2024

In today's data-driven world, where information reigns supreme, businesses rely on data to guide their decisions and strategies. However, the sheer volume and complexity of raw data from various sources can often resemble a chaotic jigsaw puzzle.

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Raw Data Data Mining Data Preparation Structured Data 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is Data Science? What are the roles and responsibilities of a Data Engineer? And many more. And many more.

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Data Engineering Data Engineer Engineering Pipeline-centric 98

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

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Manufacturing Data Warehouse Kafka Retail 108

Analytics Vidhya

FEBRUARY 25, 2023

The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

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Cloud Storage Data Lake Cloud Unstructured Data 157

Snowflake

APRIL 5, 2023

Two different data modeling approaches—dimensional data modeling and Data Vault—each have their own pros and cons. Modernizing a data warehouse with Snowflake Data Cloud is a smart investment that can provide significant benefits to businesses of all sizes, today more than ever as data models become ever more complex.

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Data Warehouse Healthcare Unstructured Data Metadata 52

Towards Data Science

MAY 3, 2023

Data Engineering Learn about slow change dimensions (SCD) and how to implement SCD Type 2 in VDK Photo by Joshua Sortino on Unsplash Data is the backbone of any organization, and in today’s fast-paced world, it is crucial to keep track of its versions. They store and manage current and historical data in a data warehouse.

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Data Lake Data SQL Data Warehouse 87

Knowledge Hut

DECEMBER 29, 2023

There are many data science fields in which experts may contribute to the success of a business, and you can hone the abilities you need by specializing in data science subfields. Data Engineering and Warehousing The data is the lifeblood of every successful Data Science endeavor.

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Data Science Data Mining Deep Learning Programming Language 98

ProjectPro

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

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Data Pipeline Architecture Kafka AWS 52

Ascend.io

JANUARY 2, 2024

The key differentiation lies in the transformational steps that a data pipeline includes to make data business-ready. Ultimately, the core function of a pipeline is to take raw data and turn it into valuable, accessible insights that drive business growth. best suit our processed data? cleaning, formatting)?

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Data Pipeline Building Raw Data Data Warehouse 52

Cloud Academy

JUNE 7, 2022

The data extraction process The first step of modeling and using data begins with extracting it from different sources and putting it in a library where it can be assessed: the Data Warehouse. In some cases, raw data is extracted and placed in the staging area; in others, a few transformations need to be performed.

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Cloud Data Warehouse Raw Data Business Intelligence 52

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.

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Data Lake Google Cloud Data Warehouse AWS 52

Monte Carlo

JULY 27, 2023

It’s not enough for data teams to be magicians transforming raw data into business value, they need to be responsible (and cost conscious) stewards as well. Unfortunately, data optimization is far from an exact science. Typically, the data warehouse, lake, or lakehouse will be a good place to start.

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Data Architecture Data Warehouse Data Engineering 52

Monte Carlo

JUNE 28, 2022

Over the past several years, cloud data lakes like Databricks have gotten so powerful (and popular) that according to Mordor Intelligence , the data lake market is expected to grow from $3.74 Traditionally, data lakes held raw data in its native format and were known for their flexibility, speed, and open source ecosystem.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

dbt Developer Hub

MARCH 9, 2022

Special Thanks: Emilie Schario, Matt Winkler dbt has done a great job of building an elegant, common interface between data engineers, analytics engineers, and any data-y role, by uniting our work on SQL. The ML engineer stepped in from here. I like to call this interoperability a “baton pass.”

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Machine Learning Raw Data SQL Python 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution. only structured data).

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution. only structured data).

Data Management 52

Data Management Management Data Lake Data Warehouse 52

ProjectPro

FEBRUARY 16, 2023

Business Intelligence and Artificial Intelligence are popular technologies that help organizations turn raw data into actionable insights. While both BI and AI provide data-driven insights, they differ in how they help businesses gain a competitive edge in the data-driven marketplace.

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Business Intelligence BI Data Mining Raw Data 52

DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users.

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Data Lake Building Raw Data ETL Tools 52