Monte Carlo

FEBRUARY 6, 2023

So, you’re planning a cloud data warehouse migration. But be warned, a warehouse migration isn’t for the faint of heart. As you probably already know if you’re reading this, a data warehouse migration is the process of moving data from one warehouse to another. A worthy quest to be sure.

Data Warehouse 52

Data Warehouse AWS Data Validation Data 52

Data Engineering Podcast

FEBRUARY 18, 2024

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). For someone who is interested in building a data lakehouse with Trino and Iceberg, how does that influence their selection of other platform elements?

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Data Lake High Quality Data Data Warehouse Google Cloud 262

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Cloudera

MAY 28, 2024

Data architecture is what defines the structures and systems within an organization responsible for collecting, storing, and accessing data, along with the policies and processes that dictate how data is governed.

Data Architecture 75

Data Architecture Architecture Data Lake Data Warehouse 75

Data Engineering Podcast

JANUARY 21, 2024

A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex.

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Data Lake High Quality Data NoSQL Data Warehouse 147

Knowledge Hut

MARCH 7, 2024

Two, it creates a commonality of data definitions, concepts, metadata and the like. The traditional data management and data warehouses, and the sequence of data transformation, extraction and migration- all arise a situation in which there are risks for data to become unsynchronized.

Big Data 98

Big Data Bytes Data Governance Raw Data 98

Monte Carlo

OCTOBER 17, 2022

The term tragedy of the commons comes from economic science and refers to situations where a common set of resources is accessed without any strong regulations or guardrails to curtail abuse. Still, the tradeoff of faster data access is often a poorly maintained data ecosystem. Now, the proper incentives are in place.

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Data Warehouse Government Data Governance Data 52

Data Engineering Podcast

FEBRUARY 4, 2024

What are the situations where RisingWave can/should be a system of record vs. a point-in-time view of data in transit, with a data warehouse/lakehouse as the longitudinal storage and query engine? What are the user/developer experience elements that you have prioritized most highly?

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SQL Data Lake High Quality Data Data Pipeline 173

Knowledge Hut

MARCH 28, 2024

Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance. Develop data models, data governance policies, and data integration strategies. Familiarity with ETL tools and techniques for data integration.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 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. Potential downsides of data lakes include governance and integration challenges.

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. Potential downsides of data lakes include governance and integration challenges.

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. Potential downsides of data lakes include governance and integration challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How do we build data products ? How can we interoperate between the data domains ? To get out of this, you have to move to another stage : the serverless stage. In this stage, you will never think about the configuration.

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Technology Architecture Google Cloud Metadata 130

Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Marts There is a thin line between Data Warehouses and Data Marts.

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Data Engineering Data Engineer Engineering Data Lake 83

Monte Carlo

MAY 14, 2024

Depending on the quantity of data flowing through an organization’s pipeline — or the format the data typically takes — the right modern table format can help to make workflows more efficient, increase access, extend functionality, and even offer new opportunities to activate your unstructured data.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

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. Does not have the resources to implement robust data governance and management.

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. Does not have the resources to implement robust data governance and management.

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. Does not have the resources to implement robust data governance and management.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

Monte Carlo

JUNE 6, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Table of Contents Why is important to answer who is responsible for data quality?

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Data Governance Government Data Data Engineering 98

Towards Data Science

JUNE 8, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Why is data quality ownership important? The governance team treats every team output as a data product.

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Data Governance Government Generalist Data Engineering 76

Precisely

OCTOBER 16, 2023

This shift has influenced how cloud resources are designed and marketed, focusing on easy access, modularity, and straightforward deployment. The data-driven enterprise : Another key trend centers around a deeper understanding of data and its vital importance in creating and sustaining competitive advantage.

Cloud 70

Cloud Cloud Computing Metadata Data Governance 70

Striim

NOVEMBER 15, 2023

Through Striim’s breakthrough of AI-ready data streaming, we’re ushering in a new era of analytics and AI, all harmonized under a single data platform on Microsoft Azure. All of these services are served by OneLake, a unified intelligent storage layer that solves the complex problem of decentralized data teams working in silos.

Retail 52

Retail BI Data Warehouse Data Lake 52

Precisely

MAY 13, 2024

Wide support for enterprise-grade sources and targets Large organizations with complex IT landscapes must have the capability to easily connect to a wide variety of data sources. Whether it’s a cloud data warehouse or a mainframe, look for vendors who have a wide range of capabilities that can adapt to your changing needs.

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Data Integration Metadata Amazon Web Services Data Governance 52

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The data is loaded as-is, without any transformation.

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Data Cleanse Raw Data Data Storage Data Warehouse 52

Monte Carlo

JANUARY 16, 2024

At the center of it all is the data warehouse, the lynchpin of any modern data stack. 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.

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

Towards Data Science

JULY 24, 2023

A data engineering manager at a Fortune 500 company expressed the pain of on-prem limitations to me by saying: “Our analysts were unable to run the queries they wanted to run when they wanted to run them. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

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Metadata Data Warehouse Data Lake Data Science 95

Data Engineering Podcast

APRIL 26, 2021

One of the most common and widely used methods of access is through a business intelligence dashboard. In this episode Maxime Beauchemin discusses how data engineers can use Superset to provide self service access to data and deliver analytics. RudderStack’s smart customer data pipeline is warehouse-first.

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Business Intelligence Data Warehouse Hadoop Data Pipeline 100

RandomTrees

FEBRUARY 6, 2024

Generative AI with Data Lineage: By automating the process of collecting lineage metadata, generating visualizations of data lineage, and identifying and troubleshooting data lineage problems. Generating new insights: Ability to generate new insights from data that would not be possible to find using traditional methods.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Monte Carlo

AUGUST 19, 2023

Frequently Asked Questions 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. It depends. They also recently acquired Apache Flink , another streaming solution.

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Building BI Data Lake Data Governance 52

phData: Data Engineering

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

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Data Lake Process Metadata Data Warehouse 52

Cloudera

JANUARY 28, 2022

Then there are the more extensive discussions – scrutiny of the overarching, data strategy questions related to privacy, security, data governance /access and regulatory oversight. These are not straightforward decisions, especially when data breaches always hit the top of the news headlines.

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Cloud Banking Data Governance Government 113

Monte Carlo

MARCH 25, 2024

.” Justin Swenson Data, Analytics, and AI Governance Lead, Sub-Zero That means Monte Carlo needs to support and integrate across those same solutions and architectures. The future of data and AI is reliability Data is becoming even more valuable and accessible than ever before.

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MySQL Government Data Governance Kafka 64

RandomTrees

FEBRUARY 29, 2024

Snowflake is a cloud-based data warehousing platform that has arisen as a primary answer for organizations hoping to use the power of data analytics in the cloud. With its adaptability, execution, and usability, Snowflake offers critical benefits over conventional on-premises data warehouses.

Data Warehouse 52

Data Warehouse Utilities Consulting Certification 52

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance. Best Practice 5.

Metadata 67

Metadata Data Governance Relational Database Government 67

Edureka

FEBRUARY 27, 2023

Self-service BI tools are becoming more popular as they allow users to access and analyze data without needing assistance from IT or a data analyst. Mobile BI is another growing trend as more people use mobile devices to access information. Data Mining: Data mining extracts valuable information from large data sets.

Business Intelligence 52

Business Intelligence BI Data Mining Data Warehouse 52

U-Next

MARCH 7, 2023

Most Data Analysts do not require a deep understanding of complex mathematics, even though they should have a foundational knowledge of statistics and mathematics. Statistics, linear algebra, and calculus are generally required for Data Analysts. Why is MS Access important in Data Analytics? What is data extraction?

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Data Mining Scala Government Data Governance 96