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

Cloudera

MAY 23, 2024

In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes.

Systems 78

Systems Building Management Data Lake 78

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

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

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

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

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 vs Data Warehouse- Schema on Read vs.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Data Engineering Podcast

AUGUST 31, 2020

Summary Data warehouse technology has been around for decades and has gone through several generational shifts in that time. The current trends in data warehousing are oriented around cloud native architectures that take advantage of dynamic scaling and the separation of compute and storage. When is Firebolt the wrong choice?

Data Warehouse 100

Data Warehouse Cloud Building Data Lake 100

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

DareData

JANUARY 30, 2023

At a critical part of a data ingestion process is the data pipeline , a series of steps that are used to gather, transform, and store data. They can be simple or complex, and they can involve multiple steps, technologies or formats such as CSV, Tabular or JSON formats. Introduction to Designing Data Lakes in AWS.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Towards Data Science

FEBRUARY 15, 2023

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. The data became useless. The proposal is simple — “Trow everything you have here inside and worry later”.

Data Lake 77

Data Lake Data Warehouse Hadoop Data Architecture 77

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

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 ETL Tools Data Warehouse Data Pipeline 52

Data Engineering Podcast

FEBRUARY 9, 2020

What are some of the foundational skills and knowledge that are necessary for effective modeling of data warehouses? How has the era of data lakes, unstructured/semi-structured data, and non-relational storage engines impacted the state of the art in data modeling?

Data Lake 100

Data Lake Data Warehouse Hadoop NoSQL 100

Cloudera

DECEMBER 11, 2020

Based in Germany, Merck KGaA is one of the leading science and technology companies, operating across healthcare, life science, and performance materials business areas. From advancing gene-editing technologies and discovering unique ways to treat the most challenging diseases, to enabling the intelligence of devices.

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

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

AltexSoft

JUNE 26, 2023

Find sources of relevant data. Choose data collection methods and tools. Decide on a sufficient data amount. Set up data storage technology. Below, we’ll elaborate on each step one by one and share our experience of data collection. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

MARCH 30, 2023

The answer is simple: They use the same technology to make the most of data. Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions. Databricks technology partners. Databricks lakehouse platform architecture.

Scala 64

Scala Data Lake BI Google Cloud 64

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. Your CTO is excited about migrating to the cloud, and constantly sends your team articles highlighting performance measurements against some of the latest technologies.

Data Lake 52

Data Lake Data Pipeline Unstructured Data 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

AUGUST 22, 2022

Gartner claims that there is such a solution capable of enabling companies to have advanced, flexible, and reusable data management across all environments. The solution is called a data fabric. to provide a unified view of all enterprise data. Support for different data types and use cases. Data fabric vs data mesh.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Snowflake

JULY 24, 2023

Around 1500 people across a wide range of roles, from accountants and financial controllers to top-level managers, rely on Fortum’s financial data, meaning it has to be highly accessible while remaining completely secure and compliant. The company also uses external tables to directly access the semi-structured data within Snowflake.

Building 52

Building Engineering Finance Data Lake 52

AltexSoft

MARCH 14, 2023

This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. to build products and services for various purposes.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

JULY 19, 2022

No matter how ridiculous it may sound, the famous quote is applicable to the technology world in many ways. 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. What data mesh is and is not.

Architecture 52

Architecture Data Lake Medical Datasets 52

AltexSoft

SEPTEMBER 23, 2021

This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. We’ll also analyze some of the data hub products existing in the market to explore how this technology works and what it actually can do. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 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.

Unstructured Data 74

Unstructured Data Data Lake Data Architecture Data 74

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. Let the data drive the data pipeline architecture.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Edureka

JUNE 1, 2023

In today’s digital era, businesses operate in a data-driven environment where data is generated at an unprecedented rate. Every single activity of our daily life is influenced by connected existence and nowadays all these activities include technology. This is where Data Analytics comes into action.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

ProjectPro

JANUARY 24, 2023

You can leverage AWS Glue to discover, transform, and prepare your data for analytics. In addition to databases running on AWS, Glue can automatically find structured and semi-structured data kept in your data lake on Amazon S3, data warehouse on Amazon Redshift, and other storage locations.

AWS 52

AWS Data Lake ETL Tools Scala 52

ProjectPro

JANUARY 27, 2023

Top ETL Business Use Cases for Streamlining Data Management Data Quality - ETL tools can be used for data cleansing, validation, enriching, and standardization before loading the data into a destination like a data lake or data warehouse.

BI 52

BI ETL Tools Retail Healthcare 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).

Big Data 78

Big Data Data Management Management Metadata 78

Rockset

APRIL 26, 2023

This is part three in Rockset’s Making Sense of Real-Time Analytics (RTA) on Streaming Data series. In part 1 , we covered the technology landscape for real-time analytics on streaming data. Offline feature store : Detecting anomalies requires historical data in order to have a baseline for comparisons.

Architecture 52

Architecture Transportation Data Lake Insurance 52

Edureka

FEBRUARY 7, 2023

One can use polybase: From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It does away with the requirement to import data from an outside source. Export information to Azure Data Lake Store, Azure Blob Storage, or Hadoop.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Cloudera

JULY 26, 2021

Once we have identified those capabilities, the second article explores how the Cloudera Data Platform delivers those prerequisite capabilities and has enabled organizations such as IQVIA to innovate in Healthcare with the Human Data Science Cloud. . Business and Technology Forces Shaping Data Product Development.

Generalist 117

Generalist Telecommunication Healthcare Data 117

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. The three most popular solutions for data storage and compute include the data warehouse, data lake, and data lakehouse.

Data Warehouse 40

Data Warehouse Building Data Lake Data Storage 40