AWS Big Data

DECEMBER 18, 2023

AWS Step Functions is a fully managed visual workflow service that enables you to build complex data processing pipelines involving a diverse set of extract, transform, and load (ETL) technologies such as AWS Glue , Amazon EMR , and Amazon Redshift. Amazon S3 hosts the metadata of all the tables as a.csv file.

Metadata 119

Metadata Visualization Data Lake Data-driven 119

AWS Big Data

MARCH 11, 2024

Establish connectivity between an Amazon QuickSight dashboard and Amazon Redshift to deliver visualization and insights. ORDERTOPIC" WHERE CAN_JSON_PARSE(kafka_value); The metadata column kafka_value that arrives from Amazon MSK is stored in VARBYTE format in Amazon Redshift.

Analytics 65

Analytics Data Warehouse Optimization Metrics 65

AWS Big Data

AUGUST 28, 2023

An example is provided below ocsf-cuid-${/class_uid}-${/metadata/product/name}-${/class_name}-%{yyyy.MM.dd} Complete the following steps to install the index templates and dashboards for your data: Download the component_templates.zip and index_templates.zip files and unzip them on your local device.

Dashboards 52

Dashboards Visualization Metadata Management 52

AWS Big Data

MARCH 22, 2023

The IdP metadata is displayed. In the SAML Certificates section, download the Federation Metadata XML file and the Certificate (Raw) file. For IdP SAML metadata under the Identity provider metadata section, choose Choose file. Choose the previously downloaded metadata file ( IIC-QuickSight.xml ). Choose Save.

Management 94

Management Metadata Enterprise Testing 94

O'Reilly on Data

JULY 25, 2023

To provide some coherence to the music, I decided to use Taylor Swift songs since her discography covers the time span of most papers that I typically read: Her main albums were released in 2006, 2008, 2010, 2012, 2014, 2017, 2019, 2020, and 2022. This choice also inspired me to call my project Swift Papers.

Consulting 246

Consulting Interactive Software IT 246

Domino Data Lab

AUGUST 8, 2019

He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything. The gist is, leveraging metadata about research datasets, projects, publications, etc., Rinse, lather, repeat—probably each week.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

JULY 22, 2019

He also really informed a lot of the early thinking about data visualization. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. He was saying this doesn’t belong just in statistics. But the point there was what was emerging was interdisciplinary.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

JUNE 2, 2019

Her talk addressed career paths for people in data science going into specialized roles, such as data visualization engineers, algorithm engineers, and so on. I recall a “Data Drinkup Group” gathering at a pub in Palo Alto, circa 2012, where I overheard Pete Skomoroch talking with other data scientists about Kahneman’s work.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

Domino Data Lab

SEPTEMBER 9, 2019

Next let’s use the displaCy library to visualize the parse tree for that sentence: In [4]: from spacy import displacy?? The displaCy library provides an excellent way to visualize named entities: In [15]: displacy.render(doc, style="ent"). metadata=convention_df["speaker"]? ). part of speech. Cupertino GPE?

Deep Learning 110

Deep Learning Machine Learning Visualization Data Science 110