Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Central to this transformation are two shifts. Let’s take a closer look.

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ProjectPro

JANUARY 31, 2022

This blog walks you through what does Snowflake do , the various features it offers, the Snowflake architecture, and so much more. Table of Contents Snowflake Overview and Architecture What is Snowflake Data Warehouse? Snowflake is not based on existing database systems or big data software platforms like Hadoop.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. How Does AWS Glue Work?

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The Modern Data Company

MARCH 1, 2024

For today’s Chief Data Officers (CDOs) and data teams, the struggle is real. We’re drowning in data yet thirsting for actionable insights. We need a new approach, a paradigm shift that delivers data with the agility and efficiency of a speedboat – enter Data Products.

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Database-centric Data Lake Government Retail 40

Ascend.io

APRIL 7, 2023

With the monolithic architectures most organizations have today, business users are stuck, constantly waiting for new data pipelines to be built or amended based on their requests. Data engineers aren’t huge fans of this paradigm either. What is a Data Mesh? Increasing speed.

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Metadata Datasets Data Governance Government 52

Monte Carlo

JUNE 28, 2022

To help organizations realize the full potential of their data lake and lakehouse investments, Monte Carlo, the data observability leader, is proud to announce integrations with Delta Lake and Databricks’ Unity Catalog for full data observability coverage. billion in 2020 to 17.60 billion in 2020 to 17.60

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Data Lake Metadata AWS Data Warehouse 52

DataKitchen

AUGUST 9, 2021

Below is our third post (3 of 5) on combining data mesh with DataOps to foster greater innovation while addressing the challenges of a decentralized architecture. We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility.

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Pharmaceutical Raw Data Data Lake Data 98

Cloudera

SEPTEMBER 15, 2022

Modern data platforms deliver an elastic, flexible, and cost-effective environment for analytic applications by leveraging a hybrid, multi-cloud architecture to support data fabric, data mesh, data lakehouse and, most recently, data observability. Luke: What is a modern data platform?

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Data Lake Analytics Application Cloud Storage Architecture 82

ProjectPro

FEBRUARY 16, 2023

This is a change from the traditional way of integrating data, which required a lot of computing processes and was limited to structured data stores. Overall, utilizing ETL architecture on AWS provides companies with a cost-efficient, scalable, and adaptable solution for processing large volumes of data.

AWS 52

AWS Data Management ETL Tools Management 52

Data Engineering Weekly

JANUARY 15, 2023

SiliconANGLE theCUBE: Analyst Predictions 2023 - The Future of Data Management By far one of the best analyses of trends in Data Management. 2023 predictions from the panel are; Unified metadata becomes kingmaker. The author writes a few best practices for managing medallion-style architecture.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Towards Data Science

APRIL 3, 2023

Zero-ETL What it is : A misnomer for one thing; the data pipeline still exists. Today, data is often generated by a service and written into a transactional database. An automatic pipeline is deployed which not only moves the raw data to the analytical data warehouse, but modifies it slightly along the way.

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Data Engineering Data Engineer Engineering Data Warehouse 98

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse?

Engineering 83

Engineering Raw Data Data Science Scala 83

Monte Carlo

AUGUST 19, 2023

Data-first companies have embraced data platforms as an effective way to aggregate, operationalize, and democratize data at scale across the organization. Whether you choose to go with a data warehouse , data lake or some combination of both is entirely up to the needs of your business.

Building 52

Building BI Data Lake Data Governance 52

Monte Carlo

JULY 30, 2020

In the age of self-service business intelligence , nearly every company considers themselves a data-first company, but not every company is treating their data architecture with the level of democratization and scalability it deserves. Your company, for one, views data as a driver of innovation. What is a data mesh?

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IT Data Lake Business Intelligence Data Architecture 45

Knowledge Hut

NOVEMBER 28, 2023

As a data engineer description, you must be ready to explore large-scale data processing and use your expertise and soft skills to ensure a scalable and reliable working environment. Data engineers need to work with large amounts of data and maintain the architectures used in various data science projects.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

ProjectPro

NOVEMBER 30, 2021

Now that we have understood how much significant role data plays, it opens the way to a set of more questions like How do we acquire or extract raw data from the source? How do we transform this data to get valuable insights from it? Where do we finally store or load the transformed data?

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Process Data Pipeline Data Warehouse AWS 52

ProjectPro

JANUARY 31, 2023

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Rockset

JANUARY 28, 2020

Aside from video data from each camera-equipped store, Standard deals with other data sets such as transactional data, store inventory data that arrive in different formats from different retailers, and metadata derived from the extensive video captured by their cameras.

Retail 40

Retail Google Cloud Raw Data Data Lake 40

Monte Carlo

JUNE 23, 2022

At some point in the last two decades, the size of our data became inextricably linked to our ego. We watched enviously as FAANG companies talked about optimizing hundreds of petabyes in their data lakes or data warehouses. We imagined what it would be like to manage big data quality at that scale.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

ProjectPro

AUGUST 24, 2021

Within no time, most of them are either data scientists already or have set a clear goal to become one. Nevertheless, that is not the only job in the data world. And, out of these professions, this blog will discuss the data engineering job role. This big data project discusses IoT architecture with a sample use case.

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Data Engineering Data Engineer Coding Project 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

Cloudera

JANUARY 21, 2021

With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Architecture overview. Separate storage.

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IT Data Lake Data Warehouse Cloud Storage 93

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more. No wonder only 0.5

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Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

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Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

AUGUST 21, 2015

Data that can be stored in traditional database systems in the form of rows and columns, for example, the online purchase transactions can be referred to as Structured Data. Data that can be stored only partially in traditional database systems, for example, data in XML records can be referred to as semi-structured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. There are several widely used unstructured data storage solutions such as data lakes (e.g., Build data architecture.

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Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

MARCH 14, 2023

Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. What is a modern data stack? A data stack, in turn, focuses on data : It helps businesses manage data and make the most out of it. Modern data stack architecture.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

MARCH 30, 2023

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. The relatively new storage architecture powering Databricks is called a data lakehouse. Databricks lakehouse platform architecture.

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