ProjectPro

JULY 22, 2016

For the same cost, organizations can now store 50 times as much data as in a Hadoop data lake than in a data warehouse. Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Christophe Blefari

JANUARY 20, 2024

He wrote some years ago 3 articles defining data engineering field. Some concepts When doing data engineering you can touch a lot of different concepts. You'll be seen as the most technical person of a data team and you'll need to help regarding "low-level" stuff you team.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

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

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. Consider whether you need a solution that supports one or multiple data formats.

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. Consider whether you need a solution that supports one or multiple data formats.

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. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Knowledge Hut

NOVEMBER 17, 2023

To provide end users with a variety of ready-made models, Azure Data engineers collaborate with Azure AI services built on top of Azure Cognitive Services APIs. Understanding data modeling concepts like entity-relationship diagrams, data normalization, and data integrity is a requirement for an Azure Data Engineer.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Knowledge Hut

MARCH 28, 2024

Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Knowledge Hut

NOVEMBER 2, 2023

These Azure data engineer projects provide a wonderful opportunity to enhance your data engineering skills, whether you are a beginner, an intermediate-level engineer, or an advanced practitioner. Who is Azure Data Engineer? Azure SQL Database, Azure Data Lake Storage).

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Data Engineering Weekly

MAY 5, 2024

link] LinkedIn: LakeChime - A Data Trigger Service for Modern Data Lakes LinkedIn points out two critical flaws in a partitioned approach to data management. The granularity of partition creation constrained data consumption.

Data Engineering 67

Data Engineering Data Engineer Engineering Google Cloud 67

Data Engineering Podcast

JANUARY 7, 2024

Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up.

Data Process 147

Data Process Process Data Lake High Quality Data 147

Data Engineering Weekly

DECEMBER 25, 2023

Integrating AI into data workflows is not just a trend but a paradigm shift, making data processes more efficient and intelligent. Lake House Architectures: The New Frontier Lakehouse architectures have been at the forefront of data engineering discussions this year.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 52

U-Next

MARCH 1, 2023

Being a hybrid role, Data Engineer requires technical as well as business skills. They build scalable data processing pipelines and provide analytical insights to business users. A Data Engineer also designs, builds, integrates, and manages large-scale data processing systems.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Knowledge Hut

NOVEMBER 17, 2023

They enhance data pipelines, transform data, and guarantee the accuracy, integrity, and compliance of the data. Their job entails Azure data engineer skills like using big data, databases, data lakes, and analytics to help firms make efficient data-driven decisions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Knowledge Hut

DECEMBER 28, 2023

An Azure Data Engineer is responsible for designing, implementing and managing data solutions on Microsoft Azure. The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Data Engineering Podcast

AUGUST 28, 2022

Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

Cloudera

AUGUST 18, 2021

In modern hybrid environments, data traverses clouds, on-premise infrastructure and IoT networks, so the process can get very complex. It requires rethinking the data lifecycle itself. . If the data goes into a data lake before analysis, extracting it can get pretty complex and time-consuming.

Medical 87

Medical Hospitality Data Lake Healthcare 87

Data Engineering Podcast

APRIL 24, 2022

In this episode Andy Dang explains why the project was created, how you can apply it to your existing data systems, and how it functions to provide detailed context for being able to gain insight into all of your data processes.

Machine Learning 100

Machine Learning Systems Data Lake Metadata 100

Knowledge Hut

OCTOBER 6, 2023

I also find Amazon Athena useful because it allows me to do ad-hoc SQL searches on data stored in Amazon S3 without the need for time-consuming ETL procedures. My ability to get practical insights, thanks to AWS Data Analytics, makes it a crucial tool for businesses wanting to leverage data for profits.

AWS 52

AWS Certification Data Analytics IT 52

Databand.ai

AUGUST 30, 2023

Challenges of Legacy Data Architectures Some of the main challenges associated with legacy data architectures include: Lack of flexibility: Traditional data architectures are often rigid and inflexible, making it difficult to adapt to changing business needs and incorporate new data sources or technologies.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Ascend.io

JANUARY 2, 2024

Destination and Data Sharing The final component of the data pipeline involves its destinations – the points where processed data is made available for analysis and utilization. Actions: Establish connections to your data sources like CRM systems or social media platforms. best suit our processed data?

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Knowledge Hut

APRIL 25, 2023

Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases. Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Knowledge Hut

MARCH 19, 2024

It also keeps backups, media files, log data, and static website content. S3 is suitable across several scenarios that utilize S3’s durability, availability, and security features, such as data archiving, content distribution, and data lake implementations, among many others.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Grouparoo

OCTOBER 6, 2021

36 Give Data Products a Frontend with Latent Documentation Document more to help everyone 37 How Data Pipelines Evolve Build ELT at mid-range and move to data lakes when you need scale 38 How to Build Your Data Platform like a Product PM your data with business. Increase visibility.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

DataKitchen

NOVEMBER 10, 2023

The Perilous State of Today’s Data Environments Data teams often navigate a labyrinth of chaos within their databases. Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team.

Data Solutions 54

Data Solutions Data Lake Data Database 54

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.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Ascend.io

APRIL 22, 2024

Many organizations start simple – ingesting data from a few sources, doing some transformations, and loading into a data warehouse. But as they add more data sources, implement real-time streaming pipelines, and build out a modern data lake, the complexity compounds quickly.

Project 52

Project Metadata Data Pipeline Data Engineering 52

The Modern Data Company

APRIL 15, 2024

The Role of a Data Model Explained Think of a data model as the ultimate organizer in the vast library of your company’s data. Its job, from its position near the end of the data processing line, is similar to that of a librarian who: Answers queries from various departments looking for specific insights.

Government 52

Government Metadata Data Data Lake 52

DataKitchen

JANUARY 25, 2022

For example, DataOps can be used to automate data integration. Previously, the consulting team had been using a patchwork of ETL to consolidate data from disparate sources into a data lake. It definitely means redeploying internal and outsourcing budgets to higher value-add activities.

Consulting 97

Consulting Recruitment Data Lake Data Engineering 97

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake? What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Knowledge Hut

SEPTEMBER 29, 2023

Understanding Big Data Services Security: If you are pursuing advanced certifications like the AWS Certified Security - Specialty, ensure you understand how to secure big data services on AWS. This includes knowledge of securing data lakes, analytics services, and real-time data processing with AWS security best practices.

AWS 52

AWS Certification Amazon Web Services Data Lake 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. End Destination Compatibility: Ensure the tool supports your target destination, be it a data warehouse, data lake, or another system.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Big Data Tools

MAY 19, 2022

Apache Hudi 1.11.0 – This release of the well-known data lake has added many interesting changes. Flink 1.15.0 – What I like about this release of Flink, a top framework for streaming data processing, is that it comes with quality documentation. data lake) formats are improving almost at the speed of thought.

Data Engineering 52

Data Engineering Data Engineer Engineering Big Data Tools 52

Big Data Tools

MAY 19, 2022

Apache Hudi 1.11.0 – This release of the well-known data lake has added many interesting changes. Flink 1.15.0 – What I like about this release of Flink, a top framework for streaming data processing, is that it comes with quality documentation. data lake) formats are improving almost at the speed of thought.

Data Engineering 52

Data Engineering Data Engineer Engineering Big Data Tools 52

Data Engineering Podcast

NOVEMBER 20, 2021

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.

Data Lake 100

Data Lake Data Integration Lambda Architecture Process 100