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

Striim

SEPTEMBER 11, 2024

Striim, for instance, facilitates the seamless integration of real-time streaming data from various sources, ensuring that it is continuously captured and delivered to big data storage targets. 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

Monte Carlo

NOVEMBER 21, 2024

This approach is fantastic when you’re not quite sure how you’ll need to use the data later, or when different teams might need to transform it in different ways. It’s more flexible than ETL and works great with the low cost of modern data storage. The data lakehouse has got you covered! ​​

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

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

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

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!

Data Warehouse 40

Data Warehouse Building Data Lake Data Storage 40

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

Towards Data Science

FEBRUARY 15, 2023

Concepts, theory, and functionalities of this modern data storage framework Photo by Nick Fewings on Unsplash Introduction I think it’s now perfectly clear to everybody the value data can have. To use a hyped example, models like ChatGPT could only be built on a huge mountain of data, produced and collected over years.

Data Lake 98

Data Lake Data Warehouse Hadoop Architecture 98

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

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

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

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

phData: Data Engineering

SEPTEMBER 19, 2023

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 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

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.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

MAY 28, 2024

Data Transformation : Clean, format, and convert extracted data to ensure consistency and usability for both batch and real-time processing. Data Loading : Load transformed data into the target system, such as a data warehouse or data lake. Used for identifying and cataloging data sources.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Rockset

JULY 29, 2022

Enterprise data warehouses (EDWs) became necessary in the 1980s when organizations shifted from using data for operational decisions to using data to fuel critical business decisions. Data warehouses are popular because they help break down data silos and ensure data consistency.

Data Analytics 52

Data Analytics Data Warehouse Datasets Cloud 52

ProjectPro

JANUARY 31, 2022

As the demand for big data grows, an increasing number of businesses are turning to cloud data warehouses. The cloud is the only platform to handle today's colossal data volumes because of its flexibility and scalability. Launched in 2014, Snowflake is one of the most popular cloud data solutions on the market.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Knowledge Hut

APRIL 25, 2023

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

RandomTrees

SEPTEMBER 6, 2020

Snowflake Overview A data warehouse is a critical part of any business organization. Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS).

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 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

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 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. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. And most of this data has to be handled in real-time or near real-time.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Edureka

FEBRUARY 9, 2023

Data engineering is a new and evolving field that will withstand the test of time and computing advances. Certified Azure Data Engineers are frequently hired by businesses to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Knowledge Hut

DECEMBER 26, 2023

According to the World Economic Forum, the amount of data generated per day will reach 463 exabytes (1 exabyte = 10 9 gigabytes) globally by the year 2025. The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

AltexSoft

SEPTEMBER 10, 2021

What is data integration and why is it important? Data integration is the process of taking data from multiple disparate internal and external sources and putting it in a single location (e.g., data warehouse ) to achieve a unified view of collected data. Key types of data integration.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

ProjectPro

SEPTEMBER 9, 2021

Apache Hive Architecture Apache Hive has a simple architecture with a Hive interface, and it uses HDFS for data storage. Data in Apache Hive can come from multiple servers and sources for effective and efficient processing in a distributed manner. Spark SQL, for instance, enables structured data processing with SQL.

Hadoop 52

Hadoop Big Data Tools Java Big Data 52

ProjectPro

JANUARY 24, 2023

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

Bytes 52

Bytes Google Cloud Data Warehouse Cloud Storage 52

Cloudera

OCTOBER 3, 2018

Google built an innovative scale-out platform for data storage and analysis in the late 1990s and early 2000s, and published research papers about their work. Today, the market includes a growing collection of companies who recognize what we both knew early — big data is a big deal.

Hadoop 75

Hadoop Cloud Data Storage Big Data 75

Edureka

JUNE 1, 2023

AWS provides services for data transfer, data storage, data lakes, big data analytics, machine learning, and everything in between that are specifically designed to deliver the greatest price-performance. By migrating to an AWS-powered solution, GERE improved deployment frequency, achieved 99.9

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

Knowledge Hut

SEPTEMBER 26, 2023

Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. Designed to tackle the challenges of modern data management and analytics, Azure Synapse brings together the worlds of big data and data warehousing into a unified and seamlessly integrated platform.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

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