Snowflake

AUGUST 24, 2023

.” homegenius’ data challenges homegenius’ data engineering team had three big data challenges it needed to solve, according to Goodrich. The data science team needed data transformations to happen quicker, the quality of data validations to be better, and the turnaround time for pipeline testing to be faster.

Data Pipeline 52

Data Pipeline Python Programming Language Data Validation 52

Ascend.io

OCTOBER 28, 2024

Filling in missing values could involve leveraging other company data sources or even third-party datasets. The cleaned data would then be stored in a centralized database, ready for further analysis. This ensures that the sales data is accurate, reliable, and ready for meaningful analysis.

Raw Data 52

Raw Data Datasets Data Pipeline Aggregated Data 52

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming, and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools. This is part of a series of articles about data quality.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools. This is part of a series of articles about data quality.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

RandomTrees

FEBRUARY 2, 2024

GPT-Based Data Engineering Accelerators: Given below is the list of some of the GPT-based data engineering accelerators. 1. DataGPT OpenAI developed DataGpt for performing data engineering tasks. Datagpt creates code for data pipelines and transformations. Its technology is based on transformer architecture.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Monte Carlo

JANUARY 10, 2024

Data Profiling 2. Data Cleansing 3. Data Validation 4. Data Auditing 5. Data Governance 6. Use of Data Quality Tools Refresh your intrinsic data quality with data observability 1. Data Profiling Data profiling is getting to know your data, warts and quirks and secrets and all.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Databand.ai

AUGUST 30, 2023

Validity: Adherence to predefined formats, rules, or standards for each attribute within a dataset. Uniqueness: Ensuring that no duplicate records exist within a dataset. Integrity: Maintaining referential relationships between datasets without any broken links.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Monte Carlo

APRIL 1, 2024

Data cleaning is an essential step to ensure your data is safe from the adage “garbage in, garbage out.” Because effective data cleaning best practices fix and remove incorrect, inaccurate, corrupted, duplicate, or incomplete data in your dataset; data cleaning removes the garbage before it enters your pipelines.

High Quality Data 52

High Quality Data Datasets Data Data Pipeline 52

Precisely

AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies.

Data Governance 52

Data Governance Government High Quality Data Datasets 52

Data Engineering Weekly

MARCH 10, 2024

Pradheep Arjunan - Shared insights on AZ's journey from on-prem to the cloud data warehouses. Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Databand.ai

AUGUST 30, 2023

Accurate data ensures that these decisions and strategies are based on a solid foundation, minimizing the risk of negative consequences resulting from poor data quality. There are various ways to ensure data accuracy. Data cleansing involves identifying and correcting errors, inconsistencies, and inaccuracies in data sets.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

JUNE 20, 2024

The quality of the input affects the quality of the output – and in order for data teams to produce high-quality data products, they need high-quality data from the very start. But, in reality, a data pipeline is more nuanced than just the quality of your internal or external data sources.

High Quality Data 52

High Quality Data Machine Learning Algorithm Datasets 52

Monte Carlo

OCTOBER 10, 2024

Table of Contents What Does an AI Data Quality Analyst Do? An AI Data Quality Analyst should be comfortable with: Data Management : Proficiency in handling large datasets. Data Cleaning and Preprocessing : Techniques to identify and remove errors. Attention to Detail : Critical for identifying data anomalies.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Monte Carlo

OCTOBER 2, 2024

Tracking data lineage is especially important when working with Python, as the language is so easy to use that you can end up digging your own grave if you start making large unintended changes to your most important datasets. Automated Tools for Python Data Lineage So how can we easily add data lineage to our Python workflows?

Python 52

Python Datasets Metadata Data 52

Airbnb Tech

NOVEMBER 28, 2023

There were several inputs that certainly could help us measure quality, but if they could not be automatically measured ( Automated ), or if they were so convoluted that data practitioners wouldn’t understand what the criterion meant or how it could be improved upon ( Actionable ), then they were discarded.

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Data Engineering Weekly

OCTOBER 30, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Towards Data Science

JULY 8, 2024

Leveraging TensorFlow Transform for scaling data pipelines for production environments Photo by Suzanne D. Williams on Unsplash Data pre-processing is one of the major steps in any Machine Learning pipeline. Tensorflow Transform helps us achieve it in a distributed environment over a huge dataset.

Data Preparation 46

Data Preparation Datasets Metadata Data Ingestion 46

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Automated profiling tools can quickly detect anomalies or patterns indicating potential dataset integrity issues.

Building 52

Building Data Cleanse Data Governance Datasets 52

Databand.ai

JULY 19, 2023

Another way data ingestion enhances data quality is by enabling data transformation. During this phase, data is standardized, normalized, and enriched. Data enrichment involves adding new, relevant information to the existing dataset, which provides more context and improves the depth and value of the data.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Inconsistent data: Inconsistencies within a dataset can indicate inaccuracies.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Databand.ai

AUGUST 30, 2023

7 Data Testing Methods, Why You Need Them & When to Use Them Helen Soloveichik August 30, 2023 What Is Data Testing? Data testing involves the verification and validation of datasets to confirm they adhere to specific requirements. In this article: Why Is Data Testing Important?

Data Validation 52

Data Validation Data Integration Data Database 52

Monte Carlo

MAY 26, 2023

Having the data warehouse as a central source of truth can help present a more consistent view of the campaign. Screenshot of Monte Carlo’s data profiling feature, which can help with data consistency. Understand and Document the Context : Data engineers may not always have the full business context behind a dataset.

Datasets 52

Datasets Data Warehouse Data Validation Data 52

Monte Carlo

FEBRUARY 15, 2023

While this is a critical step in preventing larger data incidents—it doesn’t tell you how to fix an issue once you find it. Traditionally, root-causing data quality issues required manually parsing through datasets—sometimes for weeks at a time—to discover the source of a particular data anomaly.

Data Validation 52

Data Validation Data Data Engineering Data Engineer 52

Monte Carlo

FEBRUARY 15, 2023

While this is a critical step in preventing larger data incidents—it doesn’t tell you how to fix an issue once you find it. Traditionally, root-causing data quality issues required manually parsing through datasets—sometimes for weeks at a time—to discover the source of a particular data anomaly.

Data Validation 52

Data Validation Data Engineering Data Engineer Data 52

Databand.ai

NOVEMBER 4, 2022

Re-Imagining Data Observability Ryan Yackel 2022-11-04 10:36:35 Data observability has become one of the hottest topics of the year – and for good reason. Data observability provides an end-to-end view into exactly what’s happening with data pipelines across an organization’s data fabric.

Data 52

Data Data Pipeline Retail Metadata 52

Databand.ai

JULY 3, 2023

The value of that trust is why more and more companies are introducing Chief Data Officers – with the number doubling among the top publicly traded companies between 2019 and 2021, according to PwC. In this article: Why is data reliability important? Note that data validity is sometimes considered a part of data reliability.

Data Validation 52

Data Validation Data Collection Data Machine Learning 52

Striim

JANUARY 4, 2023

Companies need to analyze large volumes of datasets, leading to an increase in data producers and consumers within their IT infrastructures. These companies collect data from production applications and B2B SaaS tools (e.g., This data makes its way into a data repository, like a data warehouse (e.g.,

PostgreSQL 52

PostgreSQL Data Warehouse Data Data Lake 52

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

RandomTrees

SEPTEMBER 6, 2020

The key features of the Data Load Accelerator include: Minimal and reusable coding: The model used is configuration-based and all data load requirements will be managed with one code base. Snowflake allows the loading of both structured and semi-structured datasets from cloud storage.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Monte Carlo

MARCH 12, 2024

Implement Custom SQL Rules Dataplex supports several custom SQL rules for data quality checks as well. Crafting custom SQL rules can help your team cater data quality checks toward your unique data validation requirements. The SQL expression should evaluate to true (pass) or false (fail) per row.

Google Cloud 52

Google Cloud Metadata SQL Data Lake 52

Monte Carlo

MAY 28, 2024

Ensuring all relevant data inputs are accounted for is crucial for a comprehensive ingestion process. Data Extraction : Begin extraction using methods such as API calls or SQL queries. Batch processing gathers large datasets at scheduled intervals, ideal for operations like end-of-day reports. A typical data ingestion flow.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Monte Carlo

OCTOBER 1, 2020

As data teams increasingly make the shift from on-prem data warehouses to Snowflake, Redshift, and other cloud warehouses or between cloud warehouses, the ability to know which data is valuable and which data can go the way of the Dodos becomes evermore important. AVG_READS_PER_DAY) Frequently updated (i.e.,

Data Warehouse 40

Data Warehouse Datasets Data Validation Machine Learning 40

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. Instead of moving customer data to the processing engine, we move the processing engine to the data. Manage data with a seamless, consistent design experience – no need for complex coding or highly technical skills.

Data Integration 52

Data Integration High Quality Data BI Data 52

Monte Carlo

FEBRUARY 6, 2023

But in reality, a data warehouse migration to cloud solutions like Snowflake and Redshift requires a tremendous amount of preparation to be successful—from schema changes and data validation to a carefully executed QA process. What’s more, issues in the source data could even be amplified by a new, sophisticated system.

Data Warehouse 52

Data Warehouse AWS Data Data Validation 52

Ascend.io

SEPTEMBER 19, 2023

Users benefit from an unobstructed, holistic view, enabling them to oversee, administer, and fine-tune the complete data pipeline effortlessly. This not only streamlines data management but also enhances the speed and accuracy of issue detection and resolution.

ETL Tools 52

ETL Tools ETL Method Data Engineering Data Engineer 52