Netflix Tech

OCTOBER 27, 2020

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy.

Data Warehouse 100

Data Warehouse Datasets Data Big Data 100

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

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. In this article, we’ll focus on a data lake vs. data warehouse.

Data Lake 59

Data Lake Data Warehouse Hadoop Raw Data 59

Knowledge Hut

APRIL 23, 2024

In the modern data-driven landscape, organizations continuously explore avenues to derive meaningful insights from the immense volume of information available. Two popular approaches that have emerged in recent years are data warehouse and big data. Data warehousing offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Sync Computing

NOVEMBER 7, 2024

Data is central to modern business and society. Depending on what sort of leaky analogy you prefer, data can be the new oil , gold , or even electricity. Of course, even the biggest data sets are worthless, and might even be a liability, if they arent organized properly.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Unstructured Data 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

Monte Carlo

OCTOBER 31, 2024

The rise of AI and GenAI has brought about the rise of new questions in the data ecosystem – and new roles. One job that has become increasingly popular across enterprise data teams is the role of the AI data engineer. Demand for AI data engineers has grown rapidly in data-driven organizations.

Data Engineer 52

Data Engineer Data Engineering Engineering Unstructured Data 52

Grouparoo

JANUARY 11, 2022

When it comes to storing large volumes of data, a simple database will be impractical due to the processing and throughput inefficiencies that emerge when managing and accessing big data. This article looks at the options available for storing and processing big data, which is too large for conventional databases to handle.

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

Data Engineering Weekly

MARCH 5, 2025

The modern data stack constantly evolves, with new technologies promising to solve age-old problems like scalability, cost, and data silos. It promised to address key pain points: Scaling: Handling ever-increasing data volumes. Speed: Accelerating data insights. Data Silos: Breaking down barriers between data sources.

Hadoop 57

Hadoop Metadata Data Ingestion Data Governance 57

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

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

NOVEMBER 21, 2024

Let’s set the scene: your company collects data, and you need to do something useful with it. Whether it’s customer transactions, IoT sensor readings, or just an endless stream of social media hot takes, you need a reliable way to get that data from point A to point B while doing something clever with it along the way.

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

Data Engineering Weekly

FEBRUARY 9, 2025

link] QuantumBlack: Solving data quality for gen AI applications Unstructured data processing is a top priority for enterprises that want to harness the power of GenAI. It brings challenges in data processing and quality, but what data quality means in unstructured data is a top question for every organization.

Data Engineer 57

Data Engineer Data Engineering Engineering Unstructured Data 57

Monte Carlo

AUGUST 25, 2023

Modern companies are ingesting, storing, transforming, and leveraging more data to drive more decision-making than ever before. Data teams need to balance the need for robust, powerful data platforms with increasing scrutiny on costs. But, the options for data storage are evolving quickly. Let’s dive in.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Towards Data Science

JANUARY 16, 2024

My personal take on justifying the existence of Data Mesh A senior stakeholder at one my projects mentioned that they wanted to decentralise their data platform architecture and democratise data across the organisation. When I heard the words ‘decentralised data architecture’, I was left utterly confused at first!

Data Warehouse 95

Data Warehouse Data Architecture Relational Database NoSQL 95

Monte Carlo

JANUARY 25, 2023

Over the past year, data contracts have taken the data world by storm as a novel approach to ensuring data quality at scale in production services. In the last couple of posts , I’ve focused on implementing data contracts in production services. Where to start implementing data contracts?

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Monte Carlo

MARCH 3, 2025

The real disruption lies with data + AI. In other words, when organizations combine their first-party data with LLMs to unlock unique insights, automate processes, or accelerate specialized workflows. We saw this with software and application observability; data and data observability; and soon data + AI and data + AI observability.

Unstructured Data 52

Unstructured Data Data Cloud Computing Banking 52

Striim

SEPTEMBER 11, 2024

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. Most importantly, these pipelines enable your team to transform data into actionable insights, demonstrating tangible business value.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Data Engineering Podcast

JUNE 17, 2021

Summary Working with unstructured data has typically been a motivation for a data lake. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable. No more scripts, just SQL.

Unstructured Data 100

Unstructured Data Data Warehouse Metadata Media 100

Cloudera

NOVEMBER 25, 2020

The challenges Matthew and his team are facing are mainly about access to a multitude of data sets, of various types and sources, with ease and ad-hoc, and their ability to deliver data-driven and confident outcomes. . Most of their research data is unstructured and has a lot of variety. Challenges Ahead.

Data Warehouse 59

Data Warehouse Unstructured Data Medical Pharmaceutical 59

U-Next

MARCH 1, 2023

Introduction Data Engineer is responsible for managing the flow of data to be used to make better business decisions. A solid understanding of relational databases and SQL language is a must-have skill, as an ability to manipulate large amounts of data effectively. In 2022, data engineering will hold a share of 29.8%

Data Engineer 75

Data Engineer Data Engineering Non-relational Database Engineering 75

ProjectPro

MAY 13, 2016

Hadoop is the most talked about innovation in the IT industry that has shaken the entire data centre infrastructure at many organizations. As the appetite for Hadoop and related big data technologies grows at an exponential rate, it is not out to spell the death of data warehousing. Data warehousing as a technology is evolving.

Data Warehouse 40

Data Warehouse Hadoop Unstructured Data Big Data 40

ProjectPro

JULY 22, 2016

The desire to save every bit and byte of data for future use, to make data-driven decisions is the key to staying ahead in the competitive world of business operations. 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 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Hevo

SEPTEMBER 10, 2024

In today’s competitive era, data is a catalyst fueling businesses to grow faster. 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.

Data Warehouse 40

Data Warehouse Architecture Structured Data Data 40

Cloudera

JUNE 11, 2024

By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and objectives. 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

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

Analytics Vidhya

FEBRUARY 25, 2023

Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

Cloud Storage 162

Cloud Storage Data Lake Cloud Unstructured Data 162

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.

Engineering 104

Engineering Raw Data Data Science Machine Learning 104

Cloudera

SEPTEMBER 17, 2020

Today’s data tool challenges. By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. .

Data Warehouse 81

Data Warehouse Unstructured Data Pharmaceutical MySQL 81

Monte Carlo

JANUARY 25, 2023

In an evolving data landscape, the explosion of new tooling solutions—from cloud-based transforms to data observability —has made the question of “build versus buy” increasingly important for data leaders. Data storage and compute are very much the foundation of your data platform. Let’s jump in!

Data Warehouse 40

Data Warehouse Building Data Lake Data Storage 40

Towards Data Science

JUNE 8, 2023

We examine which team structures are the best suited for efficiently improving data quality. Sure, data quality is everyones’ problem. Some organizations will attempt to diffuse the responsibility widely across data stewards, data owners, data engineering and governance committees, each owning a fraction of the data value chain.

Data Governance 98

Data Governance Government Generalist Data Engineer 98

Monte Carlo

JUNE 6, 2023

Sure, data quality is everyones’ problem. But who is responsible for data quality? Some organizations will attempt to diffuse the responsibility widely across data stewards, data owners, data engineering and governance committees, each owning a fraction of the data value chain.

Data Governance 98

Data Governance Government Data Data Engineer 98

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. What factors are most important when building a data management ecosystem?

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. What factors are most important when building a data management ecosystem?

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. What factors are most important when building a data management ecosystem?

Data Management 52

Data Management Management Data Lake Data Warehouse 52