Monte Carlo

JUNE 26, 2023

Why the Lakehouse Needs Data Observability Data lakes create a ton of unique challenges for data quality. Data lakes often contain larger datasets than what you’d find in a warehouse, including massive amounts of unstructured data that wouldn’t be possible in a warehouse environment.

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

Monte Carlo

MAY 31, 2022

In 2021, Vimeo moved from a process involving big complicated ETL pipelines and data warehouse transformations to one focused on data consumer defined schemas and managed self-service analytics. It involves a contract with the client sending the data , schema registry, and pipeline owners responsible for fixing any issues.

BI 52

BI Data Warehouse Unstructured Data Data Schemas 52

Databand.ai

JULY 19, 2023

Unlike the traditional Extract, Transform, Load (ETL) process, where transformations are performed before the data is loaded into the data warehouse, in ELT, transformations are performed after the data is loaded. The data pipeline should be designed to handle the volume, variety, and velocity of the data.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Monte Carlo

JUNE 28, 2022

By design, data was less structured with limited metadata and no ACID properties. As a result, data observability has become particularly important for data lake environments as they often hold large amounts of unstructured data, making data quality issues challenging to detect, resolve, and prevent.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

Cloudera

JULY 26, 2021

Auditabily: Data security and compliance constituents need to understand how data changes, where it originates from and how data consumers interact with it. a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g.,

Generalist 114

Generalist Telecommunication Healthcare Data Science 114

AltexSoft

OCTOBER 3, 2022

The logical basis of RDF is extended by related standards RDFS (RDF Schema) and OWL (Web Ontology Language). They allow for representing various types of data and content (data schema, taxonomies, vocabularies, and metadata) and making them understandable for computing systems.

Relational Database 52

Relational Database Banking Media Pharmaceutical 52