AltexSoft

AUGUST 29, 2023

Data lakes emerged as expansive reservoirs where raw data in its most natural state could commingle freely, offering unprecedented flexibility and scalability. This article explains what a data lake is, its architecture, and diverse use cases. Data warehouse vs. data lake in a nutshell.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 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

Data Engineering Podcast

JULY 10, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

ProjectPro

JANUARY 31, 2022

This blog walks you through what does Snowflake do , the various features it offers, the Snowflake architecture, and so much more. Table of Contents Snowflake Overview and Architecture What is Snowflake Data Warehouse? This is the reason why we need Data Warehouses. What is Snowflake Data Warehouse?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Precisely

OCTOBER 16, 2023

Organizations have come to understand that they can use both internal and external data to drive tremendous business value. The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale.

Cloud 70

Cloud Cloud Computing Metadata Data Governance 70

AltexSoft

FEBRUARY 11, 2023

This specialist works closely with people on both business and IT sides of a company to understand the current needs of the stakeholders and help them unlock the full potential of data. To get a better understanding of a data architect’s role, let’s clear up what data architecture is.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Cloudera

MARCH 23, 2022

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . 3: Open Performance.

Metadata 90

Metadata Data Architecture BI Machine Learning 90

Towards Data Science

JULY 24, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 95

Metadata Data Warehouse Data Lake Data Science 95

Monte Carlo

AUGUST 19, 2023

Data-first companies have embraced data platforms as an effective way to aggregate, operationalize, and democratize data at scale across the organization. I’ve also updated this post to include additional layers frequently found within a modern data platform such as data governance, access management, and machine learning.

Building 52

Building BI Data Lake Data Governance 52

AltexSoft

MARCH 14, 2023

Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. What is a modern data stack? A data stack, in turn, focuses on data : It helps businesses manage data and make the most out of it. Modern data stack architecture.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

FEBRUARY 26, 2024

A data catalog is a constantly updated inventory of the universe of data assets within an organization. It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems.

Metadata 52

Metadata Government Data Data Governance 52

Monte Carlo

DECEMBER 8, 2023

Enter: data provenance and data lineage. Data lineage is a visual tool that tracks the movement and transformations of data through various systems, processes, and applications. Data provenance is the record of metadata from data’s original sources, providing the historical context and authenticity of data.

Metadata 52

Metadata Data Data Warehouse Government 52

Monte Carlo

DECEMBER 8, 2023

Enter: data provenance and data lineage. Data lineage is a visual tool that tracks the movement and transformations of data through various systems, processes, and applications. Data provenance is the record of metadata from data’s original sources, providing the historical context and authenticity of data.

Metadata 52

Metadata Data Data Warehouse Government 52

AltexSoft

NOVEMBER 23, 2021

To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization. This post is a perfect place to learn about this approach, its architecture components, differences, benefits, tools, and more. What is data virtualization?

Process 69

Process Data Lake Metadata Data Warehouse 69

Data Engineering Podcast

NOVEMBER 9, 2020

In this episode Grant Seward explains how he built Tree Schema to be an easy to use and cost effective option for organizations to build their data catalogs. He also shares the internal architecture, how he approached the design to make it accessible and easy to use, and how it autodiscovers the schemas and metadata for your source systems.

Building 100

Building PostgreSQL BI Metadata 100

AltexSoft

MARCH 30, 2023

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. The relatively new storage architecture powering Databricks is called a data lakehouse. Databricks lakehouse platform architecture.

Scala 64

Scala Data Lake BI Google Cloud 64

Monte Carlo

JULY 18, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 52

Metadata Data Warehouse Data Lake Government 52

Monte Carlo

JANUARY 23, 2023

But while most every company would consider themselves a “data-first” organization, not every data architecture is treated to the same level of democratization and scalability. In this post we’ll look at the dizzyingly buzzy data mesh and how it stacks up to the more traditional aggregated architectural approach of a data lake.

Data Lake 52

Data Lake Unstructured Data Business Intelligence Architecture 52

Snowflake

JUNE 27, 2023

This is where Snowflake’s single, unified platform comes in: helping break down silos and simplify architectures. These customers span many countries around the world and, as such, we’ve expanded classification capabilities to support UK-, Australia-, and Canada-based data (in private preview).

Data Governance 57

Data Governance Unstructured Data Government SQL 57

Monte Carlo

AUGUST 25, 2023

Data teams need to balance the need for robust, powerful data platforms with increasing scrutiny on costs. 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. Or maybe both.)

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

JULY 18, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 40

Metadata Data Warehouse Data Lake Government 40

Monte Carlo

MARCH 16, 2023

Employee number three at Best Egg was Randy Addis, now Head of Data and Business Intelligence. Today, nearly a decade later, Randy leads over 20 people on the Best Egg data and BI teams. Randy’s team integrates application data from potential customers, credit bureaus, and other third-party sources.

Software Engineer 52

Software Engineer Software Engineering Data Governance Government 52

AltexSoft

JUNE 26, 2023

The ultimate goal of data integration is to gather all valuable information in one place, ensuring its integrity , quality, accessibility throughout the company, and readiness for BI, statistical data analysis, or machine learning. Read our articles on structured vs unstructured data and unstructured data to learn more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 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

Snowflake

MARCH 30, 2023

Automation , because the same loader patterns are used for both and the same metadata tags are expected from both, meaning the applied date timestamp in the business vault will match up with the raw date timestamp where it came from. The friction of data movement is reduced.

Engineering 83

Engineering Raw Data Data Science Scala 83

Monte Carlo

APRIL 20, 2023

I can surface ownership metadata and alert the relevant owners to make sure the appropriate changes are made so these breakages never happen. Overwhelmed data engineers need to have the proper context of the blast radius to understand which incidents need to be addressed right away, and which incidents are a secondary priority.

Data Warehouse 52

Data Warehouse BI Data Government 52

Cloudera

FEBRUARY 11, 2019

The root cause is firmly entrenched in legacy systems and traditional data governance challenges that not only result in data silos but also the misguided belief that data privacy is diametrically opposed to effective exploration of information. Governing digital transformation.

Government 57

Government Data Governance Data Science Machine Learning 57

Monte Carlo

MARCH 16, 2023

Employee number three at Best Egg was Randy Addis, now Head of Data and Business Intelligence. Today, nearly a decade later, Randy leads over 20 people on the Best Egg data and BI teams. Randy’s team integrates application data from potential customers, credit bureaus, and other third-party sources.

Software Engineer 40

Software Engineer Software Engineering Data Governance Government 40

phData: Data Engineering

JULY 18, 2023

Better Transparency: There’s more clarity about where data is coming from, where it’s going, why it’s being transformed, and how it’s being used. Improved Data Governance: This level of transparency can also enhance data governance and control mechanisms in the new data system.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

ProjectPro

FEBRUARY 8, 2017

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization 4) Big Data Grows Up : Data Governance and Security add to Enterprise Standards Data is the new oil but oil leaks can be a dangerous threat to the people surrounding it.

Big Data 40

Big Data Hadoop Data Lake Data Governance 40

Monte Carlo

APRIL 7, 2021

As the PM for Protocols , our data governance tool, I spent a lot of time thinking about and building dashboards to evaluate the quality of data our customers were sending to Segment. Your data lineage should include data products like BI reports, ML models or reverse ETL sinks to be useful.

Data Engineering 40

Data Engineering Data Engineer Engineering Datasets 40

ProjectPro

AUGUST 24, 2021

If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of best data engineering project examples below. With the trending advance of IoT in every facet of life, technology has enabled us to handle a large amount of data ingested with high velocity.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

NOVEMBER 30, 2021

In extract-transform-load (ETL), data is obtained from multiple sources, transformed, and stored in a single data warehouse, with access to data analysts , data scientists , and business analysts for data visualization and statistical analysis model building, forecasting, etc.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

MAY 17, 2023

Now, data engineers barely have to investigate the issue because the root cause is right in front of you,” said Kineret Kimhi, BI and Data Engineering Manager, BlaBlaCar. Choozle also saw an 80% reduction in overall data downtime as a result of data observability. “We It was very time-consuming. ” 36.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Now, data engineers barely have to investigate the issue because the root cause is right in front of you,” said Kineret Kimhi, BI and Data Engineering Manager, BlaBlaCar. Choozle also saw an 80% reduction in overall data downtime as a result of data observability. “We It was very time-consuming. ” 36.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

AltexSoft

OCTOBER 8, 2021

As such, you can bridge the differences between data models of source systems and destinations by matching data fields and defining data transfer frequency. Last but not least, all vendors present customers with the capability of secure data integration operations. Data profiling and cleansing. Source: G2.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52