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 Data Lake Management 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 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

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. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 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

JANUARY 29, 2024

In today's data-driven world, where information reigns supreme, businesses rely on data to guide their decisions and strategies. However, the sheer volume and complexity of raw data from various sources can often resemble a chaotic jigsaw puzzle.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Analytics Vidhya

FEBRUARY 25, 2023

The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

Cloud Storage 157

Cloud Storage Data Lake Cloud Unstructured Data 157

AltexSoft

AUGUST 29, 2023

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. This article explains what a data lake is, its architecture, and diverse use cases. Data warehouse vs. data lake in a nutshell.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

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. Read More: What is ETL?

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

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 84

Engineering Raw Data Data Science Machine Learning 84

Monte Carlo

FEBRUARY 15, 2023

Cloud data warehouses solve these problems. Belonging to the category of OLAP (online analytical processing) databases, popular data warehouses like Snowflake, Redshift and Big Query can query one billion rows in less than a minute. What is a data warehouse?

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. Data integration with ETL has changed in the last three decades. But cloud computing is preferred over the other.

AWS 52

AWS Data Management ETL Tools Management 52

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

Knowledge Hut

JUNE 28, 2023

Focus Exploration and discovery of hidden patterns and trends in data. Reporting, querying, and analyzing structured data to generate actionable insights. Data Sources Diverse and vast data sources, including structured, unstructured, and semi-structured data.

Data Mining 52

Data Mining Business Intelligence BI Structured Data 52

Edureka

AUGUST 2, 2023

Organisations and businesses are flooded with enormous amounts of data in the digital era. Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

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

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.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Workfall

JULY 4, 2023

Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights. With DBT, they weave powerful SQL spells to create data models that capture the essence of their organization’s information. The data warehouse, role, database, schema, credentials, etc.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

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

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

JANUARY 27, 2023

You have probably heard the saying, "data is the new oil". It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. Business Intelligence - ETL is a key component of BI systems for extracting and preparing data for analytics.

BI 52

BI ETL Tools Retail Healthcare 52

Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

Data Ingestion 52

Data Ingestion Data Engineer Data Engineering Engineering 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

ProjectPro

FEBRUARY 16, 2023

Business Intelligence and Artificial Intelligence are popular technologies that help organizations turn raw data into actionable insights. While both BI and AI provide data-driven insights, they differ in how they help businesses gain a competitive edge in the data-driven marketplace.

Business Intelligence 52

Business Intelligence BI Data Mining Algorithm 52

Edureka

JULY 4, 2024

For example, a retail company might use EMR to process high volumes of transaction data from hundreds or thousands of different sources (point-of-sale systems, online sales platforms, and inventory databases). Arranging the raw data could composite a 360-degree view of your sales customer integration across all channels.

AWS 52

AWS Amazon Web Services Hadoop Big Data 52

Rockset

JANUARY 5, 2022

These streams also continually deliver new fields and columns of data that can be incompatible with existing schemas. Which is why raw data streams cannot be ingested by traditional rigid SQL databases. But some newer SQL databases can ingest streaming data by inspecting the data on the fly.

Database 52

Database SQL NoSQL Raw Data 52

Edureka

JUNE 1, 2023

Data Analytics tools and technologies offer opportunities and challenges for analyzing data efficiently so you can better understand customer preferences, gain a competitive advantage in the marketplace, and grow your business. What is Data Analytics? Data analytics is the process of converting raw data into actionable insights.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

Monte Carlo

APRIL 1, 2021

is whether to choose a data warehouse or lake to power storage and compute for their analytics. While data warehouses provide structure that makes it easy for data teams to efficiently operationalize data (i.e., And it’s an increasingly relevant one for modern data teams.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 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

Monte Carlo

JUNE 14, 2023

The modern data stack era , roughly 2017 to present data, saw the widespread adoption of cloud computing and modern data repositories that decoupled storage from compute such as data warehouses, data lakes, and data lakehouses. They also recently acquired Apache Flink , another streaming solution.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Rockset

AUGUST 2, 2023

All of these assessments go back to the AI insights initiative that led Windward to re-examine its data stack. The steps Windward takes to create proprietary data and AI insights As Windward operated in a batch-based data stack, they stored raw data in S3.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52

Monte Carlo

AUGUST 31, 2023

Understanding the “rise of data downtime” With a greater focus on monetizing data coupled with the ever present desire to increase data accuracy, we need to better understand some of the factors that can lead to data downtime. We’ll take a closer look at variables that can impact your data next.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 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

Edureka

JULY 5, 2024

Tableau Prep has brought in a new perspective where novice IT users and power users who are not backward faithfully can use drag and drop interfaces, visual data preparation workflows, etc., simultaneously making raw data efficient to form insights. Considerations: Ideal for organizations already utilizing Microsoft products.

Data Preparation 40

Data Preparation Process BI ETL Tools 40

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