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

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 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 Data Lake Management Data Governance 52

Grouparoo

JANUARY 11, 2022

This article looks at the options available for storing and processing big data, which is too large for conventional databases to handle. There are two main options available, a data lake and a data warehouse. What is a Data Warehouse? What is a Data Lake?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

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

APRIL 1, 2021

Over the past few years, data lakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). While functional, our current setup for managing tables is fragmented.

Big Data 78

Big Data Data Management Management Metadata 78

Striim

SEPTEMBER 11, 2024

In an ETL-based architecture, data is first extracted from source systems, then transformed into a structured format, and finally loaded into data stores, typically data warehouses. 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

Hevo

JUNE 26, 2024

You can use data warehouses or data lakes as a repository for data management and analytics tasks. A data warehouse is the best if your organization works only with structured data. Data lake is a suitable choice if your work is based entirely on raw or […]

Data Lake 40

Data Lake IT Data Warehouse Building 40

Hevo

JUNE 26, 2024

You can use data warehouses or data lakes as a repository for data management and analytics tasks. A data warehouse is the best if your organization works only with structured data. Data lake is a suitable choice if your work is based entirely on raw or […]

Data Lake 40

Data Lake IT Data Warehouse Building 40

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.

AWS 52

AWS Data Management ETL Tools Management 52

Precisely

SEPTEMBER 9, 2024

“Enterprises are more mature in managing the quality of structured data than newer data types.” Organizations are adept at managing the quality of structured data, but management of unstructured and semi-structured data is less mature. • Invest in training and culture.

Management 64

Management High Quality Data Structured Data Data Lake 64

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

Data Engineering Podcast

AUGUST 31, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

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Data Warehouse Cloud Building Data Lake 100

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 Pipeline-centric Machine Learning 52

Data Engineering Podcast

FEBRUARY 9, 2020

If you’re struggling with unwieldy dimensional models, slow moving projects, or challenges integrating new data sources then listen in on this conversation and then give data vault a try for yourself. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council.

Data Lake 100

Data Lake Data Warehouse Hadoop NoSQL 100

Data Engineering Weekly

MAY 5, 2024

link] LinkedIn: LakeChime - A Data Trigger Service for Modern Data Lakes LinkedIn points out two critical flaws in a partitioned approach to data management. The granularity of partition creation constrained data consumption. However, the Map and Array comes with its cost.

Data Engineering 67

Data Engineering Data Engineer Engineering Google Cloud 67

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.

Unstructured Data 100

Unstructured Data Data Warehouse Metadata Media 100

Towards Data Science

FEBRUARY 15, 2023

Data by itself has no value, it needs to be organized, standardized, and clean. In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture.

Data Lake 76

Data Lake Data Warehouse Hadoop Architecture 76

Cloudera

DECEMBER 11, 2020

Without meeting GxP compliance, the Merck KGaA team could not run the enterprise data lake needed to store, curate, or process the data required to inform business decisions. It established a data governance framework within its enterprise data lake. Driving innovation with secure and governed data .

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Data Lake Government Data Security Unstructured Data 98

AltexSoft

NOVEMBER 10, 2021

Well, there’s a new phenomenon in data management that received the name of a data lakehouse. 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. Data warehouse.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

DareData

JANUARY 30, 2023

Airflow is written in Python and has a web-based user interface for managing and monitoring pipelines. AWS Glue: A fully managed data orchestrator service offered by Amazon Web Services (AWS). Azure Data Factory: A cloud-based data integration service offered by Microsoft. Stanford's Relational Databases and SQL.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Cloudera

SEPTEMBER 12, 2022

First, organizations have a tough time getting their arms around their data. More data is generated in ever wider varieties and in ever more locations. Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making. Better together.

Unstructured Data 75

Unstructured Data Data Lake Data Architecture Data 75

AltexSoft

AUGUST 22, 2022

Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes. With these challenges in enterprise data management, there has to be an approach to overcoming them, right? The solution is called a data fabric.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Striim

OCTOBER 11, 2024

This technical approach is essential for maintaining data consistency and accuracy across the pipeline. Self-Service Management Modern data pipelines facilitate seamless integration between a wide range of tools, including data integration platforms, data warehouses, data lakes, and programming languages.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Monte Carlo

OCTOBER 31, 2024

Proficiency in Programming Languages Knowledge of programming languages is a must for AI data engineers and traditional data engineers alike. In addition, AI data engineers should be familiar with programming languages such as Python , Java, Scala, and more for data pipeline, data lineage, and AI model development.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 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

ProjectPro

JANUARY 27, 2023

If you're wondering how the ETL process can drive your company to a new era of success, this blog will help you discover what use cases of ETL make it a critical component in many data management and analytic systems. 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

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. We’ll take a closer look at variables that can impact your data next. Rise of the Data Lakehouse Data warehouse or data lake?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 84

Engineering Raw Data Data Science Machine Learning 84

AltexSoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

Architecture 52

Architecture Data Lake Medical Datasets 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

Silectis

JANUARY 3, 2021

Data Architecture Data architecture is a composition of models, rules, and standards for all data systems and interactions between them. Data Catalog An organized inventory of data assets relying on metadata to help with data management. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

DECEMBER 6, 2016

News on Hadoop-November 2016 Microsoft's Hadoop-friendly Azure Data Lake will be generally available in weeks. Microsoft's cloud-based Azure Data Lake will soon be available for big data analytic workloads. Azure Data Lake will have 3 important components -Azure Data Lake Analytics, Azure Data Lake Store and U-SQL.

Hadoop 52

Hadoop Data Lake BI Big Data 52