Monte Carlo

AUGUST 2, 2022

Achieving reliable Databricks pipelines with data observability With our new partnership and updated integration , Monte Carlo provides full, end-to-end coverage across data lake and lakehouse environments powered by Databricks. “I’m excited to leverage Monte Carlo’s data observability with Databricks.”

Building 52

Building Data Lake Business Intelligence Data Pipeline 52

Monte Carlo

MARCH 28, 2024

There were a couple of challenges because it’s easy to break this type of pipeline and an analyst would work for quite a while to find the data he’s looking for.” It involves a contract with the client sending the data, schema registry, and pipeline owners responsible for fixing any issues.

Pipeline-centric 75

Pipeline-centric Engineering BI Google Cloud 75

Knowledge Hut

OCTOBER 4, 2023

Advanced Analytics and AI with Azure: Power BI requirements for dataflows can store data in Azure data lake storage Gen2. It will ingest the data through Power BI and leverage the complete power of machine learning for easy collaboration.

BI 52

BI Systems Raw Data Business Intelligence 52

Rockset

AUGUST 18, 2020

This means new data schemas, new sources and new types of queries pop up every few days. Developers need to test and iterate on new features - Your product roadmap is constantly evolving based on what your users need, and your developers want to personalize, experiment and A/B test quickly.

Data Lake 40

Data Lake Data Schemas BI Kafka 40

Databand.ai

JULY 19, 2023

Often, the extraction process includes checks and balances to verify the accuracy and completeness of the extracted data. The Load Phase After the data is extracted, it’s loaded into a data storage system in the load phase. The data is loaded as-is, without any transformation.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Monte Carlo

APRIL 24, 2023

During data ingestion, raw data is extracted from sources and ferried to either a staging server for transformation or directly into the storage level of your data stack—usually in the form of a data warehouse or data lake. There are two primary types of raw data.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

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. data warehousing).

Generalist 115

Generalist Telecommunication Healthcare Data 115

ProjectPro

JANUARY 31, 2023

It also discusses several kinds of data. Schemas are available in various shapes and sizes, and the star schema and the snowflake schema are two of the most common. Entities in a star schema are depicted as stars, whereas those in a snowflake schema are depicted as snowflakes.

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. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

Cloudera

JANUARY 11, 2021

Cloudera Data Platform architecture overcomes the barriers of affordability, rigidity, and inflexibility. Isolates workloads, while permitting a shared “data lake.” Data can be served to the business with ease. This data lake offers separate computes, but shared storage, across multiple environments.

Data Warehouse 61

Data Warehouse Pharmaceutical Data Lake BI 61