phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

AUGUST 11, 2023

Data engineering in healthcare is taking a giant leap forward with rapid industrial development. However, data collection and analysis have been commonplace in the healthcare sector for ages. Data Engineering in day-to-day hospital administration can help with better decision-making and patient diagnosis/prognosis.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

phData

JULY 17, 2023

Over the past few decades, the corporate data landscape has changed significantly. The shift from on-premise databases and spreadsheets to the modern era of cloud data warehouses and AI/ LLMs has transformed what businesses can do with data. What is the Modern Data Stack? Data modeling, data cleanup, etc.

Data Warehouse 52

Data Warehouse Data Pipeline Cloud Data ETL 52

Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

AI 88

AI AI Data Warehouse ML 88

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data scientists will typically perform data analytics when collecting, cleaning and evaluating data. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. Watsonx comprises of three powerful components: the watsonx.ai

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

JULY 25, 2024

Must Read Blogs: Exploring the Power of Data Warehouse Functionality. Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. It is commonly used in data warehouses for business analytics and reporting.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

phData

MARCH 11, 2024

For years, marketing teams across industries have turned to implementing traditional Customer Data Platforms (CDPs) as separate systems purpose-built to unlock growth with first-party data. dbt has become the standard for modeling.

Data Warehouse 52

Data Warehouse Cloud Data Data Modeling Data Models 52

phData

MAY 25, 2023

One of the easiest ways for Snowflake to achieve this is to have analytics solutions query their data warehouse in real-time (also known as DirectQuery). Creating an efficient data model can be the difference between having good or bad performance, especially when using DirectQuery.

Power BI 84

Power BI Analytics Analytics Azure 84

phData

FEBRUARY 8, 2024

Data Modeling, dbt has gradually emerged as a powerful tool that largely simplifies the process of building and handling data pipelines. dbt is an open-source command-line tool that allows data engineers to transform, test, and document the data into one single hub which follows the best practices of software engineering.

Data Modeling 52

Data Modeling Data Models Data Warehouse Database 52

Pickl AI

JUNE 7, 2024

Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. This process helps organisations manage large volumes of data efficiently.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. Jason: How do you use these models?

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. A data catalog can assist directly with every step, but model development.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

phData

NOVEMBER 2, 2023

Traditionally, the tools for batch and streaming pipelines have been distinct, and as such, data engineers have had to create and manage parallel infrastructures to leverage the benefits of batch data while still delivering low-latency streaming products for real-time use cases.

Data Pipeline 52

Data Pipeline SQL Data Warehouse Data Engineer 52

phData

AUGUST 10, 2023

Data Vault - Data Lifecycle Architecturally, let’s understand the data lifecycle in the data vault into the following layers, which play a key role in choosing the right pattern and tools to implement. Data Acquisition: Extracting data from source systems and making it accessible.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Alation

SEPTEMBER 7, 2021

With Snowflake, data stewards have a choice to leverage Snowflake’s governance policies. First, stewards are dependent on data warehouse admins to provide information and to create and edit enforcement policies in Snowflake. Alation’s data lineage helps organizations to secure their data in the Snowflake Data Cloud.

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

phData

MAY 11, 2023

One scenario could be multiple team members who will each work on ingesting and processing data from one of the source systems. Figure 3: Source Systems made into Modules Data Modeling The process to prepare data for consumption by the data visualization layer follows a highly-repeatable pattern.

ETL 52

ETL Analytics Analytics Data Modeling 52

phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Analytics Analytics SQL 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines. Saurabh Gupta is a Principal Engineer at Zeta Global.

AWS 121

AWS Machine Learning Machine Learning ML 121