Cloudera

MAY 17, 2021

Making a decision on a cloud data warehouse is a big deal. Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform.

Data Warehouse 90

Data Warehouse Cloud Government Metadata 90

Hevo

SEPTEMBER 4, 2024

A data warehouse is a centralized system that stores, integrates, and analyzes large volumes of structured data from various sources. It is predicted that more than 200 zettabytes of data will be stored in the global cloud by 2025.

Data Warehouse 72

Data Warehouse Structured Data Data Cloud 72

Snowflake

DECEMBER 4, 2024

The trend to centralize data will accelerate, making sure that data is high-quality, accurate and well managed. Overall, data must be easily accessible to AI systems, with clear metadata management and a focus on relevance and timeliness.

Unstructured Data 97

Unstructured Data Data Lake Deep Learning Structured Data 97

Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. Read Many of the preferred platforms for analytics fall into one of these two categories.

Data Lake 59

Data Lake Data Warehouse Hadoop Raw Data 59

Edureka

APRIL 22, 2025

The alternative, however, provides more multi-cloud flexibility and strong performance on structured data. Fabric is meant for organizations looking for a single pane of glass across their data estate with seamless integration and a low learning curve for Microsoft users. Next, we will see what Snowflake is What is Snowflake?

BI 52

BI Pipeline-centric Data Lake Google Cloud 52

Hevo

MAY 17, 2024

Snowflake Data Warehouse delivers essential infrastructure for handling a Data Lake, and Data Warehouse needs. It can store semi-structured and structured data in one place due to its multi-clusters architecture that allows users to independently query data using SQL.

Data Warehouse 52

Data Warehouse Data Lake Structured Data SQL 52

Knowledge Hut

APRIL 23, 2024

Two popular approaches that have emerged in recent years are data warehouse and big data. While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages. Data warehousing offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Sync Computing

NOVEMBER 7, 2024

Data volume and velocity, governance, structure, and regulatory requirements have all evolved and continue to. Despite these limitations, data warehouses, introduced in the late 1980s based on ideas developed even earlier, remain in widespread use today for certain business intelligence and data analysis applications.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 52

Grouparoo

JANUARY 11, 2022

This article looks at the options available for storing and processing big data, which is too large for conventional databases to handle. There are two main options available, a data lake and a data warehouse. What is a Data Warehouse? What is a Data Lake?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

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?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

JANUARY 25, 2023

In this article, Chad Sanderson , Head of Product, Data Platform , at Convoy and creator of Data Quality Camp , introduces a new application of data contracts: in your data warehouse. In the last couple of posts , I’ve focused on implementing data contracts in production services.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Monte Carlo

OCTOBER 31, 2024

Proficiency in Programming Languages Knowledge of programming languages is a must for AI data engineers and traditional data engineers alike. In addition, AI data engineers should be familiar with programming languages such as Python , Java, Scala, and more for data pipeline, data lineage, and AI model development.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 52

Cloudera

NOVEMBER 25, 2020

How could Matthew serve all this data, together , in an easily consumable way, without losing focus on his core business: finding a cure for cancer. The Vision of a Discovery Data Warehouse. A Discovery Data Warehouse is cloud-agnostic. Access to valuable data should not be hindered by the technology.

Data Warehouse 59

Data Warehouse Unstructured Data Medical Pharmaceutical 59

Data Engineering Podcast

FEBRUARY 9, 2020

Summary Designing the structure for your data warehouse is a complex and challenging process. As businesses deal with a growing number of sources and types of information that they need to integrate, they need a data modeling strategy that provides them with flexibility and speed.

Data Lake 100

Data Lake Data Warehouse Hadoop NoSQL 100

Data Engineering Podcast

JUNE 17, 2021

In this episode he shares the goals of the Unstruk Data Warehouse, how it is architected to extract asset metadata and build a searchable knowledge graph from the information, and the myriad ways that the system can be used. Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads?

Unstructured Data 100

Unstructured Data Data Warehouse Metadata Media 100

Towards Data Science

JANUARY 16, 2024

Evolution of the data landscape 1980s — Inception Relational databases came into existence. Result: Data warehouse was born. Data volumes started to grow. Result: The concept of Massively Parallel Processing (MPP) was introduced — data distributed across clusters. The concept of `Data Marts` was introduced.

Data Warehouse 95

Data Warehouse Data Architecture Relational Database NoSQL 95

Striim

SEPTEMBER 11, 2024

In an ETL-based architecture, data is first extracted from source systems, then transformed into a structured format, and finally loaded into data stores, typically data warehouses. This method is advantageous when dealing with structured data that requires pre-processing before storage.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Data Engineering Weekly

MARCH 5, 2025

Major data warehouse providers (Snowflake, Databricks) have released their flavors of REST catalogs, leading to compatibility issues and potential vendor lock-in. Start the Data Governance Process: Don't wait until the last minute to build the data governance framework.

Hadoop 58

Hadoop Metadata Data Ingestion Data Governance 58

Hevo

SEPTEMBER 10, 2024

As data volumes increase, fetching insights from this data comes with its challenges. Sure, you can use lakes and marts to dump any data, but ultimately, deriving business insights requires structured data with a faster querying experience. This raises the need for […]

Data Warehouse 40

Data Warehouse Architecture Structured Data Data 40

Monte Carlo

NOVEMBER 21, 2024

Data Lakehouse Pattern Data lakehouses are the sporks of architectural patterns – combining the best parts of data warehouses with data lakes. You get the structure and performance of a warehouse with the flexibility and scalability of a lake. The data lakehouse has got you covered! ​​

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

Data Engineering Weekly

FEBRUARY 9, 2025

[link] Meta: Data logs - The latest evolution in Meta’s access tools Meta writes about its access tool's system design, which helps export individual users’ access logs. link] GetInData: Data Quality in Streaming: A Deep Dive into Apache Flink Data Quality in a real-time streaming system is always challenging.

Data Engineering 58

Data Engineering Data Engineer Engineering Unstructured Data 58

Monte Carlo

MARCH 3, 2025

Prior to data powering valuable data products like machine learning models and real-time marketing applications, data warehouses were mainly used to create charts in binders that sat off to the side of board meetings. The most common themes: Data readiness- You cant have good AI with bad data.

Unstructured Data 52

Unstructured Data Data Cloud Computing Banking 52

Propel Data

OCTOBER 11, 2022

The main difference between data lakes and data warehouses is data lakes allow unstructured data, but data warehouses need structured data.

Data Lake 40

Data Lake Data Warehouse Unstructured Data Data Analytics 40

U-Next

MARCH 1, 2023

Morgan Stanley Data Engineer Interview Questions As a data engineer at Morgan Stanley, you will be responsible for creating and maintaining the infrastructure for their data warehouse. Analyzing this data often involves Machine Learning, a part of Data Science. What is a data warehouse?

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Monte Carlo

JANUARY 25, 2023

When it comes to the question of building or buying your data stack, there’s never a one-size-fits-all solution for every data team—or every component of your data stack. Data storage and compute are very much the foundation of your data platform. Let’s jump in! So, let’s take a look at each in a bit more detail.

Data Warehouse 40

Data Warehouse Building Data Lake Data Storage 40

Cloudera

JUNE 11, 2024

Structured and Unstructured Data: A Treasure Trove of Insights Enterprise data encompasses a wide array of types, falling mainly into two categories: structured and unstructured. Structured data is highly organized and formatted in a way that makes it easily searchable in databases and data warehouses.

Unstructured Data 93

Unstructured Data Pharmaceutical Banking Manufacturing 93

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value. Enter Snowpark !

Engineering 105

Engineering Raw Data Data Science Machine Learning 105

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

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

AltexSoft

AUGUST 4, 2021

Now let’s think of sweets as the data required for your company’s daily operations. Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

Towards Data Science

FEBRUARY 15, 2023

Before going into further details on Delta Lake, we need to remember the concept of Data Lake, so let’s travel through some history. The main player in the context of the first data lakes was Hadoop, a distributed file system, with MapReduce, a processing paradigm built over the idea of minimal data movement and high parallelism.

Data Lake 98

Data Lake Data Warehouse Hadoop Architecture 98

Hevo

JULY 25, 2024

A data lake is a central storage place for an organization’s data in its original format. Unlike data warehouses, data lakes can handle all kinds of data, including unstructured and semi-structured data like images, video, audio, and documents.

Data Lake 52

Data Lake Management Data Warehouse Structured Data 52

Cloudera

MAY 23, 2024

In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes.

Systems 80

Systems Building Management Data Lake 80

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52