phData

AUGUST 2, 2024

Snowflake AI Data Cloud is one of the most powerful platforms, including storage services supporting complex data. Integrating Snowflake with dbt adds another layer of automation and control to the data pipeline. Snowflake stored procedures and dbt Hooks are essential to modern data engineering and analytics workflows.

Data Pipeline 52

Data Pipeline Python Database SQL 52

phData

MARCH 1, 2024

This is incredibly useful for both Data Engineers and Data Scientists. During the development phase, Data engineers can quickly use INFER_SCHEMA to scan text files and generate DDLs. Once the table is created, the data load is as simple as using the COPY command.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

phData

OCTOBER 25, 2024

It’s common to have terabytes of data in most data warehouses, data quality monitoring is often challenging and cost-intensive due to dependencies on multiple tools and eventually ignored. This results in poor credibility and data consistency after some time, leading businesses to mistrust the data pipelines and processes.

Data Quality 52

Data Quality Data Pipeline Data Governance Database 52

Pickl AI

JUNE 7, 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. Definition and Explanation of the ETL Process ETL is a data integration method that combines data from multiple sources.

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ODSC - Open Data Science

FEBRUARY 19, 2025

Well according to Brij Kishore Pandey, it stands for Extract, Transform, Load and is a fundamental process in data engineering, ensuring data moves efficiently from raw sources to structured storage for analysis. The stepsinclude: Extraction : Data is collected from multiple sources (databases, APIs, flatfiles).

ETL 52

ETL AI AI Data Warehouse 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

phData

AUGUST 15, 2024

This blog provides an overview of applying software engineering best practices to build a test validation and monitoring suite for a non-deterministic generative AI application. Validating the Data Engineering Strategy There is no one-size-fits-all approach to chunking unstructured data.

AI 52

AI AI Data Engineering Data Engineering 52

Snorkel AI

JUNE 22, 2023

So, in those projects, you have more than 70% of the engineering development resources that are tied to data engineering activities. That is a mix of data engineering, feature engineering work, a mix of data transformation work writ large. It is at the level of data quality and joining tasks.

Data Quality 52

Data Quality ML ML AI 52

Snorkel AI

JUNE 22, 2023

So, in those projects, you have more than 70% of the engineering development resources that are tied to data engineering activities. That is a mix of data engineering, feature engineering work, a mix of data transformation work writ large. It is at the level of data quality and joining tasks.

Data Quality 52

Data Quality ML ML AI 52

Snorkel AI

JUNE 22, 2023

So, in those projects, you have more than 70% of the engineering development resources that are tied to data engineering activities. That is a mix of data engineering, feature engineering work, a mix of data transformation work writ large. It is at the level of data quality and joining tasks.

Data Quality 52

Data Quality ML ML AI 52

phData

JULY 5, 2024

To provide an example, traditional structured data such as a user’s demographic information can be provided to an AI application to create a more personable experience. Our data engineering blog in this series explores the concept of data engineering and data stores for Gen AI applications in more detail.

AI 52

AI AI Database AWS 52

phData

MAY 25, 2023

While the loss of certain DAX functions is definitely a shortcoming that we hope Microsoft will address in the near future, the impact of these lost DAX functions is not necessarily as big as you would expect. To get around losing Time Intelligence functions, a robust calendar table is suggested to reference for time-based metrics.

Power BI 84

Power BI Analytics Analytics Azure 84

Mlearning.ai

JULY 10, 2023

Without partitioning, daily data activities will cost your company a fortune and a moment will come where the cost advantage of GCP BigQuery becomes questionable. I’m personally a fan of mandatory partitioning (require partition filter) which restricts you to run a query off a table without specifying a condition on the partitioning column.

SQL 52

SQL Database Database Administration Data Lakes 52

phData

SEPTEMBER 27, 2024

All this raw data goes into your persistent stage. Then, if you later refine your definition of what constitutes an “engaged” customer, having the raw data in persistent staging allows for easy reprocessing of historical data with the new logic. Your customer data game will never be the same.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

phData

JUNE 7, 2023

The most critical and impactful step you can take towards enterprise AI today is ensuring you have a solid data foundation built on the modern data stack with mature operational pipelines, including all your most critical operational data. Data Engineer : Data Engineers are responsible for the data infrastructure.

AI 59

AI AI Machine Learning Machine Learning 59

Alation

JANUARY 17, 2023

A modern data stack can streamline IT bottlenecks, accelerating access to various teams that require data: Data analysts. Data scientists. Software engineers. Cloud engineers. Data engineers. Basically, a modern data stack can be adopted by any company that wants to improve its data management.

Data Warehouse 52

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The MLOps Blog

MARCH 20, 2023

However, in scenarios where dataset versioning solutions are leveraged, there can still be various challenges experienced by ML/AI/Data teams. Data aggregation: Data sources could increase as more data points are required to train ML models. Existing data pipelines will have to be modified to accommodate new data sources.

ML 59

ML ML Machine Learning Machine Learning 59

The MLOps Blog

MARCH 15, 2023

Our activities mostly revolved around: 1 Identifying data sources 2 Collecting & Integrating data 3 Developing Analytical/ML models 4 Integrating the above into a cloud environment 5 Leveraging the cloud to automate the above processes 6 Making the deployment robust & scalable Who was involved in the project?

AWS 52

AWS ETL ML ML 52

phData

AUGUST 17, 2023

In the rapidly evolving landscape of data engineering, Snowflake Data Cloud has emerged as a leading cloud-based data warehousing solution, providing powerful capabilities for storing, processing, and analyzing vast amounts of data. What are Orchestration Tools?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

The MLOps Blog

AUGUST 11, 2023

This section delves into the common stages in most ML pipelines, regardless of industry or business function. 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., Scikit-learn, Feature Tools) 4 Model Training (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52

The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. The following figure shows schema definition and model which reference it.

AWS 110

AWS Machine Learning Machine Learning ML 110

phData

NOVEMBER 9, 2023

Transition to the Data Cloud With multiple ways to interact with your company’s data, Snowflake has built a common access point that handles data lake access, data warehouse access, and data sharing access into one protocol. What kinds of Workloads Does Snowflake Handle?

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

Alation

FEBRUARY 17, 2023

Reichental describes data governance as the overarching layer that empowers people to manage data well ; as such, it is focused on roles & responsibilities, policies, definitions, metrics, and the lifecycle of the data. In this way, data governance is the business or process side. Communication is essential.

Data Governance 52

Data Governance Data Quality Data Analyst Data Pipeline 52

Mlearning.ai

JUNE 16, 2023

GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze. From Data Engineering to Prompt Engineering Prompt to do data analysis BI report generation/data analysis In BI/data analysis world, people usually need to query data (small/large).

AI 52

AI AI Data Analysis Data Analysis 52

phData

JULY 18, 2023

Key Advantages of Governance Simplified Change Managment: The complexity of the underlying systems is abstracted away from the user, allowing them to simply and declaratively build and change data pipelines. Testing: Data engineering should be treated as a form of software engineering.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Dataconomy

MARCH 19, 2025

Data science is an interdisciplinary field that utilizes advanced analytics techniques to extract meaningful insights from vast amounts of data. This helps facilitate data-driven decision-making for businesses, enabling them to operate more efficiently and identify new opportunities.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91