Knowledge Hut

DECEMBER 26, 2023

Data Science is a combination of several disciplines including Mathematics and Statistics, Data Analysis, Machine Learning, and Computer Science. Data Science is a huge umbrella with a plethora of roles available in the field such as a Data Scientist, Data Engineer, BI Developer, Data and Analytics Manager, etc.

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Data Science BI Business Intelligence Data Mining 97

Databand.ai

DECEMBER 13, 2022

The Top 25 Data Engineering Influencers and Content Creators on LinkedIn Ryan Yackel 2022-12-13 10:23:19 Interested in data engineering? LinkedIn is full of influencers sharing new ideas and sparking conversations on all kinds of topics, and data engineering is no exception. You’ve come to the right place.

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Data Engineering Podcast

JULY 31, 2022

In this episode Ernie Ostic shares the approach that he and his team at Manta are taking to build a complete view of data lineage across the various data systems in your organization and the useful applications of that information in the work of every data stakeholder. Can you describe how Manta is implemented?

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IT Metadata MongoDB MySQL 100

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

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard?

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Databand.ai

JULY 11, 2023

Data quality platforms can be standalone solutions or integrated into broader data management ecosystems, such as data integration, business intelligence (BI), or data analytics tools. In this article: Why Do You Need a Data Quality Platform?

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Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. This unchanging schema forms the foundation for all queries and business intelligence.

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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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AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

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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.

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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.

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Monte Carlo

JUNE 28, 2022

By design, data was less structured with limited metadata and no ACID properties. As a result, data observability has become particularly important for data lake environments as they often hold large amounts of unstructured data, making data quality issues challenging to detect, resolve, and prevent.

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Data Lake Metadata AWS Data Warehouse 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? What is data pipeline architecture?

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Towards Data Science

DECEMBER 4, 2023

A Glossary with Use Cases for First-Timers in Data Engineering An happy Data Engineer at work Are you a data engineering rookie interested in knowing more about modern data infrastructures? In this guide Data Engineering meets Formula 1. I bet you are, this article is for you!

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Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. Contents: What is the role of an Azure Data Engineer?

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Data Engineering Data Engineer Engineering Amazon Web Services 52

Data Engineering Podcast

JUNE 26, 2022

In this episode Isaac Brodsky explains how the Unfolded platform is architected, their experience joining the team at Foursquare, and how you can start using it for analyzing your spatial data today. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

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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.

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Data Engineering Podcast

NOVEMBER 27, 2022

In this episode Wes McKinney shares the ways that Arrow and its related projects are improving the efficiency of data systems and driving their next stage of evolution. Missing data? Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform!

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Data Process Process Metadata Business Intelligence 100

phData: Data Engineering

JANUARY 3, 2022

What is Data Engineering? Everything You Need to Know in 2022 Nick Goble January 4, 2022 It’s easy to overlook the amount of data that’s being generated every day — from your smartphone, your Zoom calls, to your Wi-Fi-connected dishwasher. Table of Contents What is Data Engineering What is Data Governance?

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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.

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Monte Carlo

AUGUST 25, 2023

With pre-built functionalities and robust SQL support, data warehouses are tailor-made to enable swift, actionable querying for data analytics teams working primarily with structured data. Commonly, you’ll find data formats such as JSON, Apache Parquet, and Apache Avro in these environments. Or maybe both.)

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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. ELT vs ETL. Please note!

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Monte Carlo

FEBRUARY 15, 2023

A data warehouse is an online analytical processing system that stores vast amounts of data collected within a company’s ecosystem and acts as a single source of truth to enable downstream data consumers to perform business intelligence tasks, machine learning modeling, and more. They need to be transformed.

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Data Warehouse Unstructured Data AWS Business Intelligence 52

U-Next

SEPTEMBER 7, 2022

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. The data lakes store data from a wide variety of sources, including IoT devices, real-time social media streams, user data, and web application transactions.

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Data Lake Data Warehouse Unstructured Data Amazon Web Services 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. For this task, you need a dedicated specialist — a data engineer or ETL developer.

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Monte Carlo

JUNE 26, 2023

billion in cumulative rewards to its users— Head of Data Jeff Hepburn and his team rely on Monte Carlo to deliver end-to-end visibility into the health of their data pipelines from ingestion in Databricks right down to the business intelligence layer.

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ProjectPro

JULY 27, 2021

This blog is your one-stop solution for the top 100+ Data Engineer Interview Questions and Answers. In this blog, we have collated the frequently asked data engineer interview questions based on tools and technologies that are highly useful for a data engineer in the Big Data industry.

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Knowledge Hut

SEPTEMBER 26, 2023

Organizations can harness the power of the cloud, easily scaling resources up or down to meet their evolving data processing demands. Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types.

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Monte Carlo

JANUARY 23, 2024

, Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. John agrees. “ Image courtesy of Monte Carlo.

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ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

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Knowledge Hut

SEPTEMBER 6, 2023

They highlight competence in data management, a pivotal requirement in today's business landscape, making certified individuals a sought-after asset for employers aiming to efficiently handle, safeguard, and optimize data operations. Skills acquired : Core data concepts. Data storage options. So, choose wisely.

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Database Certification MongoDB MySQL 52

AltexSoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Data lake architecture example. Poor data quality, reliability, and integrity.

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AltexSoft

JULY 29, 2022

Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture. Helpful skills.

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Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

MAY 14, 2021

On top of that, new technologies are constantly being developed to store and process Big Data allowing data engineers to discover more efficient ways to integrate and use that data. You may also want to watch our video about data engineering: A short video explaining how data engineering works.

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Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 24, 2023

Additionally, columnar storage allows BigQuery to compress data more effectively, which helps to reduce storage costs. BigQuery enables users to store data in tables, allowing them to quickly and easily access their data. It supports structured and unstructured data, allowing users to work with various formats.

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Bytes Google Cloud Data Warehouse Datasets 52

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

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ProjectPro

OCTOBER 20, 2021

Having multiple data integration routes helps optimize the operational as well as analytical use of data. Experimentation in production Big Data Data Warehouse for core ETL tasks Direct data pipelines Tiered Data Lake 4. If Data Science were a linear activity, it would be called Data Engineering.

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