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 Data Lake Management 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 Data Lake Management 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 Data Lake Management Data Warehouse 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 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 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 Data Lake Management Data Governance 52

Monte Carlo

MAY 28, 2024

This involves connecting to multiple data sources, using extract, transform, load ( ETL ) processes to standardize the data, and using orchestration tools to manage the flow of data so that it’s continuously and reliably imported – and readily available for analysis and decision-making.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 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

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. How ELT Works The process of ELT can be broken down into the following three stages: 1.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Monte Carlo

FEBRUARY 12, 2024

To differentiate and expand the usefulness of these models, organizations must augment them with first-party data – typically via a process called RAG (retrieval augmented generation). Today, this first-party data mostly lives in two types of data repositories.

Database 72

Database Unstructured Data Data Pipeline Metadata 72

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

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 Datasets 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

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 Google Cloud 52

AltexSoft

AUGUST 29, 2023

The term data lake itself is metaphorical, evoking an image of a large body of water fed by multiple streams, each bringing new data to be stored and analyzed. Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Workfall

JULY 4, 2023

With DBT, they weave powerful SQL spells to create data models that capture the essence of their organization’s information. DBT’s superpowers include seamlessly connecting with databases and data warehouses, performing amazing transformations, and effortlessly managing dependencies to ensure high-quality data.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

Cloudera

APRIL 1, 2024

The following are key attributes of our platform that set Cloudera apart: Unlock the Value of Data While Accelerating Analytics and AI The data lakehouse revolutionizes the ability to unlock the power of data.

Cloud 104

Cloud Unstructured Data Metadata Datasets 104

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

Cloudera

SEPTEMBER 17, 2020

To address this, they focused on creating an experimentation-oriented culture, enabled thanks to a cloud-native platform supporting the full data lifecycle. This platform, including an ad-hoc capable data warehouse service with built-in, easy-to-use visualization, made it easy for anyone to jump in and start experimenting.

Unstructured Data 81

Unstructured Data Data Warehouse Pharmaceutical MySQL 81

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

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 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

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

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. 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.

Scala 64

Scala Data Lake BI Google Cloud 64

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

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Data Engineering Weekly

JUNE 4, 2023

[link] Uber: Spark Analysers: Catching Anti-Patterns In Spark Apps One of the challenges in commoditizing data processing engines like Spark is that it requires an expert user to understand and operate this system. Many of the real-world data, all the way from medical images to astro monitoring, are unstructured data.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 52

Ascend.io

FEBRUARY 28, 2024

Read More: AI Data Platform: Key Requirements for Fueling AI Initiatives How Data Engineering Enables AI Data engineering is the backbone of AI’s potential to transform industries , offering the essential infrastructure that powers AI algorithms.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Knowledge Hut

MAY 31, 2023

Companies utilize different approaches to deal with data in order to extract information from structured, semi-structured, or unstructured data sets. Business Intelligence is one such approach that helps professionals to extract valuable information from structured data.

Business Intelligence 52

Business Intelligence BI Business Analyst Consulting 52

Edureka

JUNE 1, 2023

Another significant advantage of cloud-based data analytics is the ability to cease paying for infrastructure resources when they are no longer required. This happens often in data analytics since running reports on huge data processes is done once in a while. Why AWS Data Analytics?

AWS 52

AWS Data Analytics Cloud Amazon Web Services 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 Datasets 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

JUNE 14, 2023

5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

FEBRUARY 16, 2023

Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. If your database is cloud-based, using SQL to clean data is far more effective than scripting languages. They must load the raw data into a data warehouse for this analysis.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Data Visualization Graphic representation of a set or sets of data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

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 77

Data Lake Data Warehouse Hadoop Data Architecture 77