Towards Data Science

JULY 24, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 94

Metadata Data Warehouse Data Lake Data Science 94

Precisely

SEPTEMBER 11, 2023

By 2025, 80% of mainstream data quality vendors will expand their product capabilities to provide greater data insights by discovering patterns, trends, data relationships, and error resolution. Context-based bots expedite information retrieval from documentation, knowledge bases, or metadata.

Metadata 52

Metadata Data Integration Datasets Data Analysis Tools 52

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Monte Carlo

AUGUST 19, 2023

By integrating tools from a variety of vendors, a data platform enables a data engineering team to not only manage an organization’s data but also activate it for a domain’s use cases. In today’s data-driven landscape, building a data platform is no longer a nice-to-have, but a necessity for most organizations.

Building 52

Building BI Data Lake Data Governance 52

Cloudera

AUGUST 26, 2021

This means that there is out of the box support for Ozone storage in services like Apache Hive , Apache Impala, Apache Spark, and Apache Nifi, as well as in Private Cloud experiences like Cloudera Machine Learning (CML) and Data Warehousing Experience (DWX). Data ingestion through ‘s3’. Ozone Namespace Overview.

Data Science 113

Data Science Cloud Hadoop Metadata 113

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. How Apache Iceberg tables structure metadata. Without a central query log, your team can run the risk of data loss and lack of data governance.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

JULY 18, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 52

Metadata Data Warehouse Data Lake Government 52

Monte Carlo

APRIL 24, 2023

Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. Databricks Data Catalog and AWS Lake Formation are examples in this vein. AWS is one of the most popular data lake vendors.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Cloudera

FEBRUARY 16, 2022

Model interpretability is one of five main components of model governance. In this article, we explore model governance, a function of ML Operations (MLOps). Within the CML data service, model lineage is managed and tracked at a project level by the SDX. blueberry spacing) is a measure of the model’s interpretability.

Machine Learning 85

Machine Learning Algorithm Government Metadata 85

AltexSoft

MARCH 14, 2023

Also, Databricks are pioneering the lakehouse concept that makes it possible to use data management features inherent in data warehousing on the raw data stored in a low-cost data lake owing to its metadata layer. The data governance, orchestration, and monitoring component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. They dictate how data is gathered, stored, and processed and who has access to what content.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Monte Carlo

JULY 18, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care? Take data governance for example.

Metadata 40

Metadata Data Warehouse Data Lake Government 40

Monte Carlo

JULY 10, 2023

Data sampling If you’re working with large data sets where it’s impractical to evaluate every attribute or record, you can systematically sample your data set to estimate completeness. Be sure to use random sampling to select representative subsets of your data.

Data Collection 40

Data Collection Data Governance Government Data 40

Ascend.io

MARCH 14, 2023

Data Mesh Principles In the conversation around data mesh, Zhamak Dehghani highlighted that there are four fundamental principles : Business Data Domains Data as a Product Mindset Self-Serve Data Platform Federated Computational Governance 1. Thus, the data mesh follows the seams of organizational units.

Government 52

Government Architecture Data Lake Data 52

Ascend.io

MARCH 14, 2023

Data Mesh Principles In the conversation around data mesh, Zhamak Dehghani highlighted that there are four fundamental principles : Business Data Domains Data as a Product Mindset Self-Serve Data Platform Federated Computational Governance 1. Thus, the data mesh follows the seams of organizational units.

Government 52

Government Architecture Data Lake Data 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Let’s see what exactly Databricks has to offer.

Scala 64

Scala Data Lake BI Google Cloud 64

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 83

Engineering Raw Data Data Science Scala 83

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake. Continuously review and optimize costs.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

MAY 12, 2023

Tools and platforms for unstructured data management Unstructured data collection Unstructured data collection presents unique challenges due to the information’s sheer volume, variety, and complexity. The process requires extracting data from diverse sources, typically via APIs. Invest in data governance.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

JULY 29, 2022

According to the latest report by Allied Market Research , the Big Data platform will see the biggest rise in adoption in telecommunication, healthcare, and government sectors. Instruments like Apache ZooKeeper and Apache Oozie help better coordinate operations, schedule jobs, and track metadata across a Hadoop cluster.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

NOVEMBER 15, 2021

Apache Zeppelin Source: Github Apache Zeppelin is a multi-purpose notebook that supports Data Ingestion, Data Discovery, Data Analytics , Data Visualization , and Data Collaboration. Calcite has chosen to stay out of the data storage and processing business.

Big Data 52

Big Data Project Metadata Programming Language 52

Cloudera

DECEMBER 20, 2021

But balancing a strong layer of security and governance with easy access to data for all users is no easy task. Another option — a more rewarding one — is to include centralized data management, security, and governance into data projects from the start. …and congratulations to the winner: Bank of the West.

Government 91

Government Data Security Banking Metadata 91

phData: Data Engineering

AUGUST 4, 2023

Snowflake hides user data objects and makes them accessible only through SQL queries through the compute layer. It handles the metadata related to these objects, access control configurations, and query optimization statistics.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Cloudera

OCTOBER 11, 2021

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

Data Engineering 95

Data Engineering Data Engineer Cloud Engineering 95

phData: Data Engineering

JULY 18, 2023

Why Migrate to a Modern Data Stack? Enterprises can effortlessly prepare data and construct ML models without the burden of complex integrations while maintaining the highest level of security. Improved Data Governance: This level of transparency can also enhance data governance and control mechanisms in the new data system.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52