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

Monte Carlo

JULY 10, 2023

Accuracy reflects the degree to which the data correctly describes the “real-world” objects being described. For example, let’s say a streaming provider has 10 million overall subscribers who can access its content. According to the CRM’s data set, the streaming provider has 13 million subscribers.

Data Collection 40

Data Collection Data Governance Government Data 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 40

Software Engineer Software Engineering Data Governance Government 40

dbt Developer Hub

JULY 2, 2023

Agile development ​ dbt Cloud includes built-in Git with accessible features directly from its IDE, which simplifies development immensely. Once a developer is happy with their additions or changes to the Data Vault codebase, they can commit the code within the IDE and open a Pull Request, triggering a code review process.

Cloud 52

Cloud Data Warehouse Data BI 52

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

Data Engineering Podcast

NOVEMBER 26, 2023

Summary Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. Go to [materialize.com]([link] today and get 2 weeks free!

Architecture 147

Architecture Data Lake High Quality Data SQL 147

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

Cloudera

MARCH 23, 2022

Apache Iceberg is a high-performance, open table format, born-in-the cloud that scales to petabytes independent of the underlying storage layer and the access engine layer. By being a truly open table format, Apache Iceberg fits well within the vision of the Cloudera Data Platform (CDP). What is Apache Iceberg? 2: Open formats.

Metadata 88

Metadata Data Architecture BI Machine Learning 88

DataKitchen

FEBRUARY 2, 2023

A combined, interoperable suite of tools for data team productivity, governance, and security for large and small data teams. Ultimately, there will be an interoperable toolset for running the data team , just like a more focused toolset (ELT/Data Science/BI) for acting upon data.

Metadata 52

Metadata BI Government ETL Tools 52

Monte Carlo

NOVEMBER 6, 2023

They can range in terms of complexity, ease of use, and feature sets, but all are designed to help illuminate the dark corners of your data repositories, and are a critical component of your data governance practice. Here’s an overview of ten popular data discovery tools (in no particular order) that are available today.

Metadata 40

Metadata Unstructured Data Government Data Governance 40

Towards Data Science

MAY 19, 2023

Business users are unable to find and access data assets critical to their workflows. Data engineers spend countless hours troubleshooting broken pipelines. Absence or poor adoption of company-wide guidelines surrounding the creation and deployment of data products. Is it the responsibility of the data product managers?

Pipeline-centric 105

Pipeline-centric Data Software Engineer Software Engineering 105

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 97

Metadata Data Warehouse Data Lake Data Science 97

Monte Carlo

AUGUST 30, 2023

You will also need to securely store and provide dbt with the necessary credentials to access your target database. Make sure logs are accessible for future reference. This will help you debug any failures and understand the health of your data pipeline over time. Also, remember data governance.

SQL 52

SQL Datasets Database High Quality Data 52

AltexSoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Key features of a data lakehouse. Schema enforcement and data governance. ACID transaction support. DataFrame API support.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

JUNE 14, 2023

This frequently involves, in some order, extraction (from a source system), transformation (where data is combined with other data and put into the desired format), and loading (into storage where it can be accessed). Most organizations deploy some or all of these data pipeline architectures.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. 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.

Process 69

Process Data Lake Metadata Data Warehouse 69

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. Data governance As a data management framework, feature stores must consider data privacy and data governance.

Engineering 83

Engineering Raw Data Data Science Scala 83

AltexSoft

JUNE 26, 2023

Commonly, the entire flow is fully automated and consists of three main steps — data extraction, transformation, and loading ( ETL or ELT , for short, depending on the order of the operations.) Dive deeper into the subject by reading our article Data Integration: Approaches, Techniques, Tools, and Best Practices for Implementation.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

JULY 29, 2022

Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. a suitable technology to implement data lake architecture. What happens, when a data scientist, BI developer , or data engineer feeds a huge file to Hadoop? Let’s see why.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

SEPTEMBER 23, 2021

It’s not a single technology, but rather an architectural approach that unites storages, data integration and orchestration tools. With a data hub, businesses receive the means to structure, and harmonize information collected from various sources. Storage layer corresponds to the needs of database management and data modeling.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Cloudera

MARCH 6, 2018

Instead, they have separate data stores and inconsistent (if any) frameworks for data governance, management, and security. If catalog metadata and business definitions live with transient compute resources, they will be lost, requiring work to recreate later and making auditing impossible.

Cloud 40

Cloud Metadata Big Data AWS 40

ProjectPro

FEBRUARY 8, 2017

The need for speed to use Hadoop for sentiment analysis and machine learning has fuelled the growth of hadoop based data stores like Kudu and adoption of faster databases like MemSQL and Exasol. Apache Sentry enforces role based authorization to the metadata and data stored in a Hadoop cluster.

Big Data 40

Big Data Hadoop Data Lake Data Governance 40

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 56

Government Data Governance Data Science Machine Learning 56

ProjectPro

JANUARY 31, 2022

This is the reason why we need Data Warehouses. What is Snowflake Data Warehouse? A Data Warehouse is a central information repository that enables Data Analytics and Business Intelligence (BI) activities. Snowflake Data Marketplace gives users rapid access to various third-party data sources.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

AUGUST 24, 2021

Data Factory to orchestrate regular runs of the Azure Machine Learning model. Power BI dashboard to display and drill down the predictions. 4) Event Data Analysis NYC Open Data is free public data published by New York City agencies and partners. Machine Learning web service to host forecasting code.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

OCTOBER 8, 2021

At the same time, you get rid of the “data silos” problem: When no team or department has a unified view of all data due to fragments being locked in separate databases with limited access. Instead, companies can opt for automated data integration tools and platforms that often present no-code or low-code environments.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 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

Databand.ai

DECEMBER 29, 2022

On LinkedIn, he posts frequently about big data, master data, data science, data management, and data storytelling. She’s trained thousands on how to strategically use the power of data visualization to enhance the decision-making process.

BI 52

BI Consulting Data Science Data Governance 52

Snowflake

JUNE 27, 2023

Additional pruning features, now GA, help reduce the need to scan across entire data sets, thereby enabling faster searches. To help customers more easily analyze the structure of expensive queries and identify operators that cause performance problems, we will soon be making Programmatic Access to Query Profile available in GA.

Data Governance 57

Data Governance Unstructured Data Government SQL 57

Monte Carlo

JANUARY 23, 2024

Data engineering was a thankless but increasingly important job, with data engineering teams straddling between building infrastructure, running jobs, and fielding ad-hoc requests from the analytics and BI teams. As a result, being a data engineer was both a blessing and a curse.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

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

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 72

Cloud Cloud Computing Metadata Data Governance 72

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

AUGUST 19, 2023

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

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