Cloudera

OCTOBER 7, 2022

Cloudera’s mission, values, and culture have long centered around using open source engines on open data and table formats to enable customers to build flexible and open data lakes. The Open Data Lakehouse . dbt used in transformation pipelines on data warehouses (Image source: [link].

Data Warehouse 81

Data Warehouse Data Lake Government High Quality Data 81

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Precisely

JULY 18, 2024

A data fabric offers several key benefits that transform your data management: Accelerates analytics and decision-making processes by enhancing data accessibility through seamless data integration and retrieval across diverse environments. Increase metadata maturity.

Data Management 52

Data Management Management Metadata Database-centric 52

DataKitchen

JANUARY 3, 2022

They need high-quality data in an answer-ready format to address many scenarios with minimal keyboarding. What they are getting from IT and other data sources is, in reality, poor-quality data in a format that requires manual customization. IT-created infrastructure such as a data lake/warehouse).

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98

Monte Carlo

AUGUST 31, 2023

Data pipelines can handle both batch and streaming data, and at a high-level, the methods for measuring data quality for either type of asset are much the same. We’ll take a closer look at variables that can impact your data next. Rise of the Data Lakehouse Data warehouse or data lake?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

FEBRUARY 23, 2024

And this renewed focus on data quality is bringing much needed visibility into the health of technical systems. As generative AI (and the data powering it) takes center stage, it’s critical to bring this level of observability to where your data lives, in your data warehouse , data lake , or data lakehouse.

Systems 64

Systems Building Retail Data Lake 64

Monte Carlo

AUGUST 30, 2024

It’s our goal at Monte Carlo to provide data observability and quality across the enterprise by monitoring every system vital in the delivery of data from source to consumption. We started with popular modern data warehouses and quickly expanded our support as data lakes became data lakehouses.

SQL 40

SQL Engineering Data Lake High Quality Data 40

Edureka

FEBRUARY 7, 2023

They should also be proficient in programming languages such as Python , SQL , and Scala , and be familiar with big data technologies such as HDFS , Spark , and Hive. Learn programming languages: Azure Data Engineers should have a strong understanding of programming languages such as Python , SQL , and Scala.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

AltexSoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Monte Carlo

AUGUST 10, 2023

While different solutions or tools may have significant differences in features offered, there is no real difference between data observability and data reliability engineering. Both terms are focused on the practice of ensuring healthy, high quality data across an organization. It is still relevant today.

Data Pipeline 98

Data Pipeline Software Engineering Software Engineer Machine Learning 98

Monte Carlo

FEBRUARY 23, 2024

And this renewed focus on data quality is bringing much needed visibility into the health of technical systems. As generative AI (and the data powering it) takes center stage, it’s critical to bring this level of observability to where your data lives, in your data warehouse , data lake , or data lakehouse.

Systems 52

Systems Building Retail Data Lake 52

Databand.ai

JULY 5, 2023

They provide insights into the health of data integration processes, detect issues in real-time, and enable teams to optimize data flows. Data lake and data warehouse monitoring: These tools monitor the performance, storage, and access patterns of data lakes and data warehouses, ensuring optimal performance and data availability.

Data Pipeline 40

Data Pipeline Data Lake Data Warehouse Datasets 40

Monte Carlo

JUNE 23, 2022

At some point in the last two decades, the size of our data became inextricably linked to our ego. We watched enviously as FAANG companies talked about optimizing hundreds of petabyes in their data lakes or data warehouses. We imagined what it would be like to manage big data quality at that scale.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

DataKitchen

JULY 27, 2023

Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. Azure Blob Storage serves as the data lake to store raw data. It does the job.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

DareData

JULY 29, 2020

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines.

Data Pipeline 52

Data Pipeline Healthcare Medical Pipeline-centric 52

Monte Carlo

APRIL 26, 2022

Similarly, the discipline of DataOps helps teams remove silos and work more efficiently to deliver high-quality data products across the organization. DataOps professionals also leverage observability to decrease downtime as companies begint o ingest large amounts of data from various sources.

IT 64

IT Data Pipeline Data Engineering Data Engineer 64

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

Striim

JANUARY 8, 2025

Key components include: ETL Tools: To extract, transform, and load data from systems such as enterprise resource planning (ERP) software, customer relationship management (CRM) platforms, and other operational systems. BI Platforms: For data visualization and reporting.

BI 52

BI Business Analyst Retail Raw Data 52