Rockset

AUGUST 27, 2020

In several of these scenarios, both NoSQL databases and data lakes have been very useful because of their schemaless nature, variable cost profiles and scalability characteristics. This allows us to correct bad predictions made by the AI via our custom tagging app, tapping into the latest data ingested in our pipeline.

NoSQL 40

NoSQL Data Lake Portfolio Architecture 40

dbt Developer Hub

JULY 2, 2023

Each house does not have a pipe directly from the local river: there is a dam and a reservoir to collect water for the city from all of the sources – the lakes, streams, creeks, and glaciers – before the water is redirected into each neighborhood and finally into each home’s taps. A new development in the city? No problem!

Cloud 52

Cloud Data Warehouse Data BI 52

LinkedIn Engineering

JULY 27, 2023

The Extract phase utilizes Azure Data Factory to manage data ingestion from sources like Azure Kusto Clusters, Delta Live Tables in Azure Databricks, LinkedIn's internal REST endpoints, and Azure Data Lake. Change tracking information driven watermarking: Pros: Pipeline idempotent, relies on source-provided delta records.

Metadata 75

Metadata Utilities Cloud Data Lake 75

Snowflake

NOVEMBER 2, 2023

These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh. Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements.

Data Lake 87

Data Lake Data Warehouse Cloud Unstructured Data 87

Towards Data Science

MARCH 9, 2023

When working with NLP applications it gets even deeper with stages like stemming, lemmatization, stop word removal, tokenization, vectorization, and part of speech tagging (POS tagging). It is perfectly possible to execute these steps using libraries like Pandas and NumPy or NLTK and SpaCy for NLP.

Machine Learning 82

Machine Learning Building Datasets Scala 82

Snowflake

MARCH 30, 2023

Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse? 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.

Engineering 82

Engineering Raw Data Data Science Scala 82

phData: Data Engineering

JANUARY 3, 2022

Data engineers allow an organization to efficiently and effectively collect data from various sources, generally storing that data into a data lake or into several Kafka topics. The ELT use case is commonly seen within data lake architectures or systems that need raw extracted data from multiple sources.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52