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

Cloudera

NOVEMBER 17, 2022

The Solution: CDP Private Cloud brings a next-generation hybrid architecture with cloud-native benefits to HBL’s data platform. HBL started their data journey in 2019 when data lake initiative was started to consolidate complex data sources and enable the bank to use single version of truth for decision making.

Banking 85

Banking Management Data Lake Professional Services 85

ProjectPro

FEBRUARY 8, 2023

Another leading European company, Claranet, has adopted Glue to migrate their data load from their existing on-premise solution to the cloud. The popular data integration tool, AWS Glue, enables data analytics users to quickly acquire, analyze, migrate, and integrate data from multiple sources.

AWS 98

AWS Scala Metadata Data Lake 98

Striim

SEPTEMBER 11, 2024

This method is advantageous when dealing with structured data that requires pre-processing before storage. Conversely, in an ELT-based architecture, data is initially loaded into storage systems such as data lakes in its raw form. Would the data be stored on cloud or on-premises?’

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Sync Computing

JANUARY 27, 2025

They offer a high memory-to-CPU ratio, with configurations providing up to 1 Terabyte of memory, making them ideal for in-memory databases, big data analytics, and real-time processing. Ideal for real-time analytics, high-performance caching, or machine learning, but data does not persist after instance termination.

AWS 52

AWS Data Lake Big Data Machine Learning 52

U-Next

MARCH 1, 2023

Slowly Changing Dimensions (SCDs) are data warehouse dimensions that store and manage both current and historical data over time. What is a data lake, and how does it differ from a data warehouse? Streaming Data Warehouses offer real-time computing and allow users to use offline data warehouse functions online.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Snowflake

JANUARY 26, 2023

Cybersecurity will finally join the modern data stack Omer Singer , Head of Cybersecurity Strategy at Snowflake, predicts that 2023 will see more security teams leveraging modern cloud data lakes , which provide a consolidated view of all security data, alongside business and IT data, to improve an organization’s security posture.

Retail 93

Retail Healthcare Unstructured Data Media 93

Data Engineering Weekly

MARCH 3, 2024

The migration enhanced data quality, lineage visibility, performance improvements, cost reductions, and better reliability and scalability, setting a robust foundation for future expansions and onboarding.

Data Engineering 67

Data Engineering Data Engineer Engineering Pipeline-centric 67

Rockset

JULY 29, 2022

So, it’s not real-time data. Amazon Redshift Amazon Redshift cloud data warehouse is a fully-managed SQL analytics service. It analyzes structured and unstructured data from other warehouses, operational databases, and data lakes. This is true for the three data warehouses mentioned above.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 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. This feature allows for a more flexible exploration of data.

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. This feature allows for a more flexible exploration of data.

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. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Management Data Lake Data Governance 52

AltexSoft

AUGUST 4, 2021

Since data marts provide analytical capabilities for a restricted area of a data warehouse, they offer isolated security and isolated performance. Data mart vs data warehouse vs data lake vs OLAP cube. Data lakes, data warehouses, and data marts are all data repositories of different sizes.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

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

Knowledge Hut

JANUARY 18, 2024

Splunk Splunk is an American software company broadening its horizon in monitoring, investigating, and analyzing data. Splunk is the leading software to convert any data into real-world action. You can search structured as well as unstructured data with Splunk. A data lakehouse combines a data warehouse and a data lake.

Data Science 98

Data Science Amazon Web Services Big Data Finance 98

ProjectPro

MARCH 17, 2014

However, while you might be familiar with what is big data and hadoop, there is high probability that other people around you are not really sure on –What is big data, what hadoop is, what big data analytics is or why it is important. Table of Contents What is Big Data and what is the Big Deal?

Hadoop 52

Hadoop Big Data Unstructured Data Data Analytics 52

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

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 Machine Learning Pipeline-centric 52

Monte Carlo

MARCH 28, 2024

In 2021, Vimeo moved from a process involving big complicated ETL pipelines and data warehouse transformations to one focused on data consumer defined schemas and managed self-service analytics. And, of course, experience in data analytics, pipelines, or other forms of data management is vital.

Engineering 75

Engineering Pipeline-centric BI Google Cloud 75

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 Machine Learning BI 64

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 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.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

Cloudera

NOVEMBER 15, 2024

However, there is a fundamental challenge standing in the way of being successful: data. Unstructured data not ready for analysis: Even when defenders finally collect log data, it’s rarely in a format that’s ready for analysis.

Metadata 59

Metadata Unstructured Data Data Lake Government 59

Knowledge Hut

JUNE 26, 2023

Here are some data engineering project ideas to consider and Data Engineering portfolio project examples to demonstrate practical experience with data engineering problems. Realtime Data Analytics Project Overview: Olber, a corporation that provides taxi services, is gathering information about each and every journey.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

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

Knowledge Hut

DECEMBER 22, 2023

Power BI is a phenomenal tool for organizations looking to launch, prioritize and grow their business through data-driven intelligence efforts. Microsoft AI’s latest features allow even non-data scientists to prepare data, build machine learning models, find insights from structured and unstructured data.

BI 98

BI Business Intelligence Machine Learning SQL 98

Cloudera

AUGUST 21, 2020

Perhaps one of the most significant contributions in data technology advancement has been the advent of “Big Data” platforms. Historically these highly specialized platforms were deployed on-prem in private data centers to ensure greater control , security, and compliance. Streaming data analytics. .

Hadoop 76

Hadoop Big Data Cloud Kafka 76

Knowledge Hut

NOVEMBER 7, 2023

The applications of cloud computing in businesses of all sizes, types, and industries for a wide range of applications, including data backup, email, disaster recovery, virtual desktops big data analytics, software development and testing, and customer-facing web apps. What Is Cloud Computing?

Cloud Computing 98

Cloud Computing Cloud Amazon Web Services Entertainment 98

AltexSoft

NOVEMBER 23, 2021

But this data is all over the place: It lives in the cloud, on social media platforms, in operational systems, and on websites, to name a few. Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization.

Process 69

Process Data Lake Metadata Data Warehouse 69

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. Zero ETL is a bit of a misnomer.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

APRIL 1, 2016

(Source: [link] ) Hadoop is powering the next generation of Big Data Analytics. NetworkAsia.net Hadoop is emerging as the framework of choice while dealing with big data. Four years ago Centrica was struggling hard on how to deal with the exponential increase in big data. March 11, 2016. March 31, 2016. Computing.co.uk

Hadoop 52

Hadoop BI Big Data Big Data Tools 52

RandomTrees

SEPTEMBER 27, 2024

This eliminates the need to make multiple copies of data assets. Unified data platform: One Lake provides a unified platform for all data types, including structured, semi-structured, and unstructured data. What’s new ever since Fabric’s GA announcement in November 2023?

Database-centric 52

Database-centric Pipeline-centric IT BI 52

ProjectPro

APRIL 2, 2018

News on Hadoop - March 2018 Kyvos Insights to Host Session "BI on Big Data - With Instant Response Times" at the Gartner Data and Analytics Summit 2018.PRNewswire.com, Source : [link] ) The data lake continues to grow deeper and wider in the cloud era.Information-age.com, March 5 , 2018.

Hadoop 40

Hadoop Data Lake Relational Database Big Data 40

AltexSoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Monte Carlo

AUGUST 31, 2023

Data pipelines can handle both batch and streaming data, and at a high-level, the methods for measuring data quality for either type of asset are much the same. What is Data Quality? Data quality as a concept is not novel – “ data quality ” has been around as long as humans have been collecting data!

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

NOVEMBER 17, 2023

Data Warehouses: This service is used for storing relational, structured tables. Azure Data Lake : Azure data lake can store both structured and unstructured data. It is mainly used for data analytics and exploration. It is generally used for business reporting.

Data Lake 52

Data Lake Programming Language Certification Java 52

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. ELT allows them to work with the data directly.

Process 52

Process Building Raw Data Data Lake 52