Rocket-Powered Data Science

SEPTEMBER 20, 2021

The way that I explained it to my data science students years ago was like this. They realized that the search results would probably not provide an answer to my question, but the results would simply list websites that included my words on the page or in the metadata tags: “Texas”, “Cows”, “How”, etc. What is a semantic layer?

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

O'Reilly on Data

JANUARY 29, 2019

In other words, could we see a roadmap for transitioning from legacy cases (perhaps some business intelligence) toward data science practices, and from there into the tooling required for more substantial AI adoption? Data scientists and data engineers are in demand.

Deep Learning 271

Deep Learning Machine Learning Data Science Metadata 271

O'Reilly on Data

FEBRUARY 11, 2020

They don’t have the resources they need to clean up data quality problems. The building blocks of data governance are often lacking within organizations. These include the basics, such as metadata creation and management, data provenance, data lineage, and other essentials. And that’s just the beginning.

Data Quality 363

Data Quality Metadata Data Governance Publishing 363

Octopai

DECEMBER 12, 2021

What enables you to use all those gigabytes and terabytes of data you’ve collected? Metadata is the pertinent, practical details about data assets: what they are, what to use them for, what to use them with. Without metadata, data is just a heap of numbers and letters collecting dust. Where does metadata come from?

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Metadata Management Data Quality Data Governance 98

Octopai

OCTOBER 18, 2021

First, what active metadata management isn’t : “Okay, you metadata! Now, what active metadata management is (well, kind of): “Okay, you metadata! Data assets are tools. Metadata are the details on those tools: what they are, what to use them for, what to use them with. . Quit lounging around!

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Metadata Management IT Data Quality 98

O'Reilly on Data

JANUARY 8, 2019

Beyond investments in narrowing the skills gap, companies are beginning to put processes in place for their data science projects, for example creating analytics centers of excellence that centralize capabilities and share best practices. Automation in data science and data. Burgeoning IoT technologies.

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Machine Learning IoT Internet of Things Data Science 272

Analytics Vidhya

SEPTEMBER 13, 2022

This article was published as a part of the Data Science Blogathon. Introduction The purpose of a data warehouse is to combine multiple sources to generate different insights that help companies make better decisions and forecasting. It consists of historical and commutative data from single or multiple sources.

Data Warehouse 357

Data Warehouse Forecasting Data Science Big Data 357

Data Science 101

DECEMBER 16, 2019

Even though Amazon is taking a break from announcements (probably focusing on Christmas shoppers), there are still some updates in the cloud data science world. If you would like to get the Cloud Data Science News as an email, you can sign up for the Cloud Data Science Newsletter. Here they are.

Data Science 81

Data Science Machine Learning Metadata Data Lake 81

O'Reilly on Data

APRIL 2, 2019

A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. Metadata and artifacts needed for audits: as an example, the output from the components of MLflow will be very pertinent for audits.

Machine Learning 271

Machine Learning Modeling Data Science Software 271

AWS Big Data

NOVEMBER 19, 2024

Pricing and availability Amazon MWAA pricing dimensions remains unchanged, and you only pay for what you use: The environment class Metadata database storage consumed Metadata database storage pricing remains the same. Over the years, he has helped multiple customers on data platform transformations across industry verticals.

Metadata 110

Metadata Cost-Benefit Metrics Optimization 110

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 279

Machine Learning Software Metadata Testing 279

Cloudera

AUGUST 26, 2021

Learn more about the impacts of global data sharing in this blog, The Ethics of Data Exchange. Before we jump into the data ingestion step, here is a quick overview of how Ozone manages its metadata namespace through volumes, buckets and keys. . Data ingestion through ‘s3’. Ozone Namespace Overview.

Data Science 113

Data Science Forecasting Metadata Machine Learning 113

Rocket-Powered Data Science

JULY 6, 2021

This is accomplished through tags, annotations, and metadata (TAM). granules) of the data collection for fast search, access, and retrieval is also important for efficient orchestration and delivery of the data that fuels AI, automation, and machine learning operations. Collect, curate, and catalog (i.e.,

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

O'Reilly on Data

MAY 21, 2019

As the number of data scientists and machine learning engineers grow within an organization, tools have to be standardized, models and features need to be shared, and automation starts getting introduced. 58% of survey respondents indicated they are building or evaluating data science platforms. Data results from a Twitter poll.

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Machine Learning Technology Deep Learning Data Science 279

Rocket-Powered Data Science

MARCH 10, 2020

The training data and feature sets that feed machine learning algorithms can now be immensely enriched with tags, labels, annotations, and metadata that were inferred and/or provided naturally through the transformation of your repository of data into a graph of data.

Metadata 250

Metadata Machine Learning Prescriptive Analytics ROI 250

Rocket-Powered Data Science

FEBRUARY 15, 2023

These rules are not necessarily “Rocket Science” (despite the name of this blog site), but they are common business sense for most business-disruptive technology implementations in enterprises. Love thy data: data are never perfect, but all the data may produce value, though not immediately.

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Strategy Experimentation Uncertainty Machine Learning 290

Data Virtualization

DECEMBER 8, 2022

Reading Time: 3 minutes While cleaning up our archive recently, I found an old article published in 1976 about data dictionary/directory systems (DD/DS). Nowadays, we no longer use the term DD/DS, but “data catalog” or simply “metadata system”. It was written by L.

Metadata 69

Metadata IT Publishing Data Integration 69

DataKitchen

FEBRUARY 2, 2023

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. And the tools for acting on data are consolidating: Tableau does data prep, Altreyx does data science, Qlik joined with Talend, etc.

Metadata 130

Metadata Testing Data Science Risk 130

DataRobot Blog

MARCH 16, 2023

Traditionally, developing appropriate data science code and interpreting the results to solve a use-case is manually done by data scientists. The integration allows you to generate intelligent data science code that reflects your use case. Data scientists still need to review and evaluate these results.

Data Science 98

Data Science Technology Data-driven Modeling 98

CIO Business Intelligence

NOVEMBER 27, 2024

For AI, there’s no universal standard for when data is ‘clean enough.’ A lot of organizations spend a lot of time discarding or improving zip codes, but for most data science, the subsection in the zip code doesn’t matter,” says Kashalikar. Missing trends Cleaning old and new data in the same way can lead to other problems.

Enterprise 141

Enterprise Data Quality Structured Data Modeling 141

Smart Data Collective

APRIL 5, 2022

The data science profession has become highly complex in recent years. Data science companies are taking new initiatives to streamline many of their core functions and minimize some of the more common issues that they face. IBM Watson Studio is a very popular solution for handling machine learning and data science tasks.

Machine Learning 145

Machine Learning Cost-Benefit Data Science Unstructured Data 145

TDAN

SEPTEMBER 3, 2019

As I write this, I can almost hear you wail “No, no, we don’t have too much metadata, we don’t have nearly enough! We have several projects in flight to expand our use of metadata.” Sorry, I’m going to have to disagree with you there. You are on a fool’s errand that will just provide […].

Metadata 65

Metadata Data Governance Big Data Modeling 65

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

Metadata 52

Metadata Management Data Lake Data Governance 52

Domino Data Lab

JUNE 26, 2019

Even the most sophisticated data science organizations struggle to keep track of their data science projects. But while there are a legion of tools for individual data scientists, the needs of data science leaders have not been well-served. They need help tracking projects.

Data Science 65

Data Science Dashboards Metadata Snapshot 65

Data Virtualization

JULY 28, 2023

Reading Time: 2 minutes As the volume, variety, and velocity of data continue to surge, organizations still struggle to gain meaningful insights. This is where active metadata comes in. Listen to “Why is Active Metadata Management Essential?” What is Active Metadata? ” on Spreaker.

Metadata 52

Metadata Data Integration Management Data Science 52

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. BTW, videos for Rev2 are up: [link]. On deck this time ’round the Moon: program synthesis.

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Metadata Data Science Machine Learning Data-driven 105

Rocket-Powered Data Science

JULY 19, 2023

That is not a totally clear separation and distinction, but it might help to clarify their different applications of data science. Data scientists work with business users to define and learn the rules by which precursor analytics models produce high-accuracy early warnings.

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Data-driven Enterprise Analytics Machine Learning 188

AWS Big Data

OCTOBER 25, 2023

But most important of all, the assumed dormant value in the unstructured data is a question mark, which can only be answered after these sophisticated techniques have been applied. Therefore, there is a need to being able to analyze and extract value from the data economically and flexibly. The solution integrates data in three tiers.

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Unstructured Data Metadata Management Analytics 133

AWS Big Data

JUNE 7, 2023

They chose AWS Glue as their preferred data integration tool due to its serverless nature, low maintenance, ability to control compute resources in advance, and scale when needed. To share the datasets, they needed a way to share access to the data and access to catalog metadata in the form of tables and views.

Metadata 58

Metadata Data Lake Machine Learning Big Data 58

DataKitchen

AUGUST 9, 2021

The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. The domain requires a team that creates/updates/runs the domain, and we can’t forget metadata: catalogs, lineage, test results, processing history, etc., ….

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Testing Data Lake Metadata Publishing 246

DataKitchen

JANUARY 5, 2021

Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Each of the roles mentioned above views the world through a preferred set of tools: Data Center/IT – Servers, storage, software.

Testing 147

Testing Data Governance Metadata Visualization 147

Timo Elliott

JANUARY 6, 2022

The data you’ve collected and saved over the years isn’t free. If storage costs are escalating in a particular area, you may have found a good source of dark data. Analyze your metadata. If you’ve yet to implement data governance, this is another great reason to get moving quickly.

IT 135

IT Metadata Data-driven Data Governance 135

CIO Business Intelligence

MAY 24, 2022

The company said that IDMC for Financial Services has built-in metadata scanners that can help extract lineage, technical, business, operational, and usage metadata from over 50,000 systems (including data warehouses and data lakes) and applications including business intelligence, data science, CRM, and ERP software.

Finance 140

Finance Management Metadata Machine Learning 140

AWS Big Data

DECEMBER 4, 2024

SageMaker Lakehouse enables seamless data access directly in the new SageMaker Unified Studio and provides the flexibility to access and query your data with all Apache Iceberg-compatible tools on a single copy of analytics data. Having confidence in your data is key.

Data Analytics 103

Data Analytics Analytics Data Lake Data Quality 103

Cloudera

NOVEMBER 13, 2020

Data science experiment result and performance analysis, for example, calculating model lift. While plan time statistics are unreliable, an execution engine that adapts in real-time based on actual data means that the right optimization can be applied dynamically when the query seems to be taking longer than it should.

Optimization 143

Optimization Metadata Statistics Cost-Benefit 143

CIO Business Intelligence

OCTOBER 13, 2023

The data architect also “provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy and related business architecture,” according to DAMA International’s Data Management Body of Knowledge.

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CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist salary. Semi-structured data falls between the two.

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Unstructured Data Data Analytics Analytics Data Science 137