Knowledge Hut

JUNE 20, 2023

A novice data scientist prepared to start a rewarding journey may need clarification on the differences between a data scientist and a machine learning engineer. Many people are learning data science for the first time and need help comprehending the two job positions.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Maxime Beauchemin

AUGUST 28, 2017

This post follows up on The Rise of the Data Engineer , a recent post that was an attempt at defining data engineering and described how this new role relates to historical and modern roles in the data space. Understanding and exposing the adversity that the role is facing is a first step towards finding solutions.

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Data Engineering Data Engineer Engineering Software Engineer 174

Knowledge Hut

FEBRUARY 27, 2023

A data engineer is a key member of an enterprise data analytics team and is responsible for handling, leading, optimizing, evaluating, and monitoring the acquisition, storage, and distribution of data across the enterprise. Data Engineers indulge in the whole data process, from data management to analysis.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Knowledge Hut

JUNE 30, 2023

The demand for experienced data engineers continuously expands in today's data-driven environment. Books on data engineering serve as essential resources to guide you through the vast terrain of data engineering. What is Data Engineering? Who are Data Engineers?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 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.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

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?

Data Warehouse 100

Data Warehouse Raw Data Data Lake BI 100

Knowledge Hut

JANUARY 18, 2024

A data scientist’s job description focuses on the following – Automating the collection process and identifying the valuable data. Collection of large amounts of raw data, facts, and figures and converting them into a usable format Use of data-driven techniques to solve business-related issues and problems.

Data Science 98

Data Science Business Analyst ETL Method Data Architect 98

AltexSoft

JANUARY 26, 2022

In recent years, it’s getting more common to see organizations looking for a mysterious analytics engineer. As you may guess from the name, this role sits somewhere in the middle of a data analyst and data engineer, but it’s really neither one nor the other. What an analytics engineer is. Sounds interesting, right?

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

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. This data is pulled directly by end users through SQL queries or through Business Intelligence tools.

Cloud 52

Cloud Data Warehouse Raw Data Business Intelligence 52

Knowledge Hut

DECEMBER 28, 2023

Its comprehensive suite of services can handle data at scale. It’s no surprise that the demand for certified Azure data engineers has skyrocketed. Today, Azure Data Engineer certification is an invaluable asset for those looking to excel in the field of data engineering. Who is an Azure Data Engineer?

Certification 52

Certification Data Engineering Data Engineer Engineering 52

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.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Monte Carlo

FEBRUARY 15, 2023

Cloud data warehouses solve these problems. Belonging to the category of OLAP (online analytical processing) databases, popular data warehouses like Snowflake, Redshift and Big Query can query one billion rows in less than a minute. What is a data warehouse? What is a data warehouse?

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. 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.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

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.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Snowflake

FEBRUARY 21, 2023

I led the developer experience team and the Voice product (one of the main revenue engines), and I founded the AI/ML business unit. Sub-second response times: It provides a powerful cache that automatically syncs and serves your Snowflake data with blazing-fast queries to your apps. I started programming when I was 14.

AWS 122

AWS Raw Data Building Architecture 122

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Grouparoo

AUGUST 3, 2021

The mission of many data teams is a very simple one. They seek to use data to help the business take smarter actions. The input is raw data from everywhere that touches the business. Because there are often many sources for the data, much of the work is often in data engineering.

Raw Data 52

Raw Data Data Warehouse SQL Coding 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Monte Carlo

AUGUST 19, 2023

The data platform manages the collection, normalization, transformation, and application of data for a given data product—from business insights and dashboards to ML and AI engineering. So, with all that ambiguity, where’s a data engineer to start?Even We’ll cover: What is a data platform?

Building 52

Building BI Data Lake Data Governance 52

RandomTrees

NOVEMBER 29, 2023

Here the practice of data warehousing and warehouse system is very important and the use of right modelling techniques has become a very important factor in todays’ competitive world. In this choice, Big Data will play an important role and its choice is also inevitably crucial in the Business Intelligence and related systems.

Data Warehouse 52

Data Warehouse Data Lake Database-centric Data Cleanse 52

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.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

Knowledge Hut

OCTOBER 30, 2023

For any organization to grow, it requires business intelligence reports and data to offer insights to aid in decision-making. This data and reports are generated and developed by Power BI developers. The work of a Power BI developer is to take data in its raw form, derive meaning, and make sense of it.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 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?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

U-Next

MARCH 7, 2023

Taking data from sources and storing or processing it is known as data extraction. Define Data Wrangling The process of data wrangling involves cleaning, structuring, and enriching raw data to make it more useful for decision-making. Data is discovered, structured, cleaned, enriched, validated, and analyzed.

Data Mining 96

Data Mining Scala Government Data Governance 96

Monte Carlo

APRIL 24, 2023

While we won’t get into the minutia of every consideration for every level of the data stack, it’s important to recall these five considerations as they’ll nonetheless steer the direction of our conversation. Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Monte Carlo

MARCH 14, 2023

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley.

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Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

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Scala Data Lake BI Google Cloud 64

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers. What is Real Time Data Warehousing?

Data Warehouse 94

Data Warehouse Kafka Lambda Architecture Telecommunication 94

ProjectPro

SEPTEMBER 26, 2021

According to a survey, big data engineering job interviews increased by 40% in 2020 compared to only a 10% rise in Data science job interviews. Table of Contents Big Data Engineer - The Market Demand Who is a Big Data Engineer? Most of these are performed by Data Engineers.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

ProjectPro

AUGUST 24, 2021

Nevertheless, that is not the only job in the data world. Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JANUARY 27, 2023

Whether you are a data engineer, BI engineer, data analyst, or an ETL developer, understanding various ETL use cases and applications can help you make the most of your data by unleashing the power and capabilities of ETL in your organization. You have probably heard the saying, "data is the new oil".

BI 52

BI ETL Tools Retail Healthcare 52

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. The ELT workflow. ELT vs ETL. Scalability.

Process 52

Process Building Raw Data Data Lake 52

dbt Developer Hub

APRIL 19, 2023

The goal of dimensional modeling is to take raw data and transform it into Fact and Dimension tables that represent the business. The business user has context around the business objectives and business processes, and can provide you with that information.

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Building PostgreSQL BI Database 145

Grouparoo

JANUARY 26, 2022

Warehouse Storage Needs The foundation of Maslow’s hierarchy of needs is about physiological needs like having enough food, water, and sleep. In the world of the modern data stack, the parallel is having a data warehouse that can act as a foundation for the work to be done. In our data stack, we have similar goals.

Data Warehouse 52

Data Warehouse Food ETL Tools Raw Data 52

Monte Carlo

JUNE 14, 2023

For example, data may be extracted from multiple sources, reorganized, and joined at different times before ultimately being served to its final destination. Why is data pipeline architecture important? Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Data integration , on the other hand, happens later in the data management flow.

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Data Collection Machine Learning Unstructured Data Non-relational Database 52