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 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

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

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

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 Engineer 52

Data Engineer Data Engineering Engineering Unstructured Data 52

Monte Carlo

JANUARY 16, 2024

In this blog post, we’ll look at six innovations that are shaping the future of the data warehousing, as well as challenges and considerations that organizations should keep in mind. Data lake and data warehouse convergence 2. Easier to stream real-time data 3. Zero-copy data sharing 4.

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

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 .

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

DareData

JANUARY 30, 2023

AWS Glue: A fully managed data orchestrator service offered by Amazon Web Services (AWS). Talend Data Fabric: A comprehensive data management platform that includes a range of tools for data integration, data quality, and data governance. Introduction to Designing Data Lakes in AWS.

Data Engineer 52

Data Engineer Data Engineering NoSQL Engineering 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

JUNE 26, 2023

From the perspective of data science, all miscellaneous forms of data fall into three large groups: structured, semi-structured, and unstructured. Key differences between structured, semi-structured, and unstructured data. Note, though, that not any type of web scraping is legal.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 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

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

Knowledge Hut

OCTOBER 30, 2023

Data Transformation and ETL: Handle more complex data transformation and ETL (Extract, Transform, Load) processes, including handling data from multiple sources and dealing with complex data structures. Ensure compliance with data protection regulations. Define data architecture standards and best practices.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Snowflake

MARCH 30, 2023

Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse? Schema drift on a wide table structure needs an ALTER TABLE statement, whereas the tall table structure does not. Yes, feature stores are part of the MLOps discipline. Enter Snowpark !

Engineering 84

Engineering Raw Data Data Science Machine Learning 84

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

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

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. As data becomes more and more foundational to business, data teams will only grow.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

APRIL 25, 2023

It provides a flexible data model that can handle different types of data, including unstructured and semi-structured data. Key features: Flexible data modeling High scalability Support for real-time analytics 4. Key features: Instant elasticity Support for semi-structured data Built-in data security 5.

Data Engineer 52

Data Engineer Data Engineering Engineering Google Cloud 52

Ascend.io

JULY 19, 2023

But something about data mesh feels different, doesn’t it? For one, data mesh tackles the real headaches caused by an overburdened data lake and the annoying game of tag that’s too often played between the people who make data, the ones who use it, and everyone else caught in the middle.

Data Governance 40

Data Governance Government Metadata Data 40

Cloudera

OCTOBER 30, 2018

The abundant growth of data, maturation of machine algorithms, and future regulatory compliance demands from the European Union’s General Data Protection Regulation (GDPR) will shift the landscape for creating a single source of the truth for customer data. Additionally, they only provide one piece of the puzzle.

Unstructured Data 42

Unstructured Data Data Lake Algorithm Machine Learning 42

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Knowledge Hut

DECEMBER 26, 2023

Down below, we have listed some books that are widely used to learn about web scraping:- Web Scraping with Python: Collecting More Data from the Modern Web (Author: Ryan Mitchell) Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale (Author: Jay M.

Data Science 52

Data Science Programming Language Scala R (Programming) 52

AltexSoft

OCTOBER 8, 2021

They are applied to retrieve data from the source systems, perform transformations when necessary, and load it into a target system ( data mart , data warehouse, or data lake). So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

JANUARY 31, 2022

It also offers a unique architecture that allows users to quickly build tables and begin querying data without administrative or DBA involvement. Snowflake is a cloud-based data platform that provides excellent manageability regarding data warehousing, data lakes, data analytics, etc. What Does Snowflake Do?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

AUGUST 24, 2021

Google BigQuery receives the structured data from workers. Finally, the data is passed to Google Data studio for visualization. In this project, you will explore the usage of Databricks Spark on Azure with Spark SQL and build this data pipeline. Upload it to Azure Data lake storage manually.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

ProjectPro

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. The bedrock of Apache Spark is Spark Core, which is built on RDD abstraction.

Big Data 52

Big Data Project Metadata Programming Language 52

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

AltexSoft

MARCH 14, 2023

Built around a cloud data warehouse, data lake, or data lakehouse. Modern data stack tools are designed to integrate seamlessly with cloud data warehouses such as Redshift, Bigquery, and Snowflake, as well as data lakes or even the child of the first two — a data lakehouse.

IT 59

IT Data Warehouse Data Governance Data Lake 59

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 Machine Learning 64

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Architecture 124

Architecture Metadata Kafka Government 124