Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? To make things a little easier, I’ve outlined the six must-have layers you need to include in your data platform and the order in which many of the best teams choose to implement them. The five must-have layers of a modern data platform Second to “how do I build my data platform?”,

Building 52

Building BI Data Lake Data Governance 52

AltexSoft

MAY 12, 2023

For example, developers can use Twitter API to access and collect public tweets, user profiles, and other data from the Twitter platform. Data ingestion tools are software applications or services designed to collect, import, and process data from various sources into a central data storage system or repository.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more. Find sources of relevant data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

SEPTEMBER 23, 2021

Data integration layer holds any transformations required to make the data digestible for end users. This often involves such operations as data harmonization, mastering, and enrichment with metadata. Storage layer corresponds to the needs of database management and data modeling. Data hub architecture.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

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

ProjectPro

NOVEMBER 30, 2021

Now that we have understood how much significant role data plays, it opens the way to a set of more questions like How do we acquire or extract raw data from the source? How do we transform this data to get valuable insights from it? Where do we finally store or load the transformed data?

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

JANUARY 31, 2022

Provides Powerful Computing Resources for Data Processing Before inputting data into advanced machine learning models and deep learning tools, data scientists require sufficient computing resources to analyze and prepare it. They can also design and run data apps and securely share, gather, and commercialize real-time data.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Monte Carlo

JULY 30, 2020

Unlike traditional monolithic data infrastructures that handle the consumption, storage, transformation, and output of data in one central data lake, a data mesh supports distributed, domain-specific data consumers and views “data-as-a-product,” with each domain handling their own data pipelines.

IT 45

IT Data Lake Business Intelligence Data Architecture 45