Towards AI

FEBRUARY 11, 2025

Beyond Scale: Data Quality for AI Infrastructure The trajectory of AI over the past decade has been driven largely by the scale of data available for training and the ability to process it with increasingly powerful compute & experimental models. Author(s): Richie Bachala Originally published on Towards AI.

Data Quality 53

Data Quality Data Engineering Data Engineering Data Engineering 53

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Also Read: Top 10 Data Science tools for 2024.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. To learn more, see the documentation. To learn more, see the documentation.

AWS 98

AWS ML ML Data Quality 98

Dataconomy

SEPTEMBER 4, 2023

Text analytics: Text analytics, also known as text mining, deals with unstructured text data, such as customer reviews, social media comments, or documents. It uses natural language processing (NLP) techniques to extract valuable insights from textual data. Poor data integration can lead to inaccurate insights.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

AWS Machine Learning Blog

FEBRUARY 18, 2025

To handle the log data efficiently, raw logs were centralized into an Amazon Simple Storage Service (Amazon S3) bucket. An Amazon EventBridge schedule checked this bucket hourly for new files and triggered log transformation extract, transform, and load (ETL) pipelines built using AWS Glue and Apache Spark.

AWS 78

AWS Database ETL AI 78

AWS Machine Learning Blog

FEBRUARY 21, 2025

The agent knowledge base stores Amazon Bedrock service documentation, while the cache knowledge base contains curated and verified question-answer pairs. For this example, you will ingest Amazon Bedrock documentation in the form of the User Guide PDF into the Amazon Bedrock knowledge base. This will be the primary dataset.

AWS 129

AWS Natural Language Processing Machine Learning Machine Learning 129

Smart Data Collective

APRIL 29, 2020

The storage and processing of data through a cloud-based system of applications. Master data management. The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). Data transformation. Microsoft Azure.

Data Warehouse 98

Data Warehouse SQL Azure ETL 98

phData

JUNE 23, 2023

Data lineage plays a crucial role in providing transparency, ensuring data integrity, and enabling informed decision-making. While traditional methods of tracking data lineage often involve manual documentation and complex processes, the Snowflake Data Cloud offers a powerful and streamlined solution.

Data Quality 52

Data Quality Data Governance ETL Database 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

AWS 128

AWS Machine Learning Machine Learning ML 128

ODSC - Open Data Science

OCTOBER 18, 2024

Set specific, measurable targets Data science goals to “increase sales” lack the clarity needed to evaluate success and secure ongoing funding. Audit existing data assets Inventory internal datasets, ETL capabilities, past analytical initiatives, and available skill sets. Complexity limits accessibility and value creation.

Data Science 52

Data Science Data Scientist Analytics Analytics 52

Pickl AI

MARCH 19, 2025

It is widely used for storing and managing structured data, making it an essential tool for data engineers. MongoDB MongoDB is a NoSQL database that stores data in flexible, JSON-like documents. Apache Spark Apache Spark is a powerful data processing framework that efficiently handles Big Data.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

AUGUST 9, 2024

Document Hierarchy Structures Maintain thorough documentation of hierarchy designs, including definitions, relationships, and data sources. This documentation is invaluable for future reference and modifications. Data Quality Issues Inconsistent or incomplete data can hinder the effectiveness of hierarchies.

Data Warehouse 52

Data Warehouse Data Quality ETL Business Intelligence 52

phData

AUGUST 10, 2023

Implement business rules and validations: Data Vault models often involve enforcing business rules and performing data quality checks. Leverage dbt’s `test` macros within your models and add constraints to ensure data integrity between data vault entities. This is where automation tools come into play.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Pickl AI

AUGUST 11, 2024

Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance. Introduction In today’s data-driven landscape, organisations are increasingly reliant on Data Analytics to inform decision-making and drive business strategies.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

For instance, a notebook that monitors for model data drift should have a pre-step that allows extract, transform, and load (ETL) and processing of new data and a post-step of model refresh and training in case a significant drift is noticed. Refer to SageMaker documentation for detailed instructions.

ML 120

ML ML Data Scientist Python 120

The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. If you aren’t aware already, let’s introduce the concept of ETL. Redshift, S3, and so on.

AWS 52

AWS ETL ML ML 52

IBM Journey to AI blog

JULY 3, 2024

As data types and applications evolve, you might need specialized NoSQL databases to handle diverse data structures and specific application requirements. With an open data lakehouse, you can access a single copy of data wherever your data resides.

AI 45

AI AI Data Quality Database 45

Dataconomy

JULY 28, 2023

Data preprocessing is essential for preparing textual data obtained from sources like Twitter for sentiment classification ( Image Credit ) Influence of data preprocessing on text classification Text classification is a significant research area that involves assigning natural language text documents to predefined categories.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

phData

OCTOBER 17, 2024

Apache Airflow Airflow is an open-source ETL software that is very useful when paired with Snowflake. dbt offers a SQL-first transformation workflow that lets teams build data transformation pipelines while following software engineering best practices like CI/CD, modularity, and documentation.

AI 52

AI AI SQL Data Quality 52

Pickl AI

JANUARY 28, 2025

Data can be structured (e.g., documents and images). The diversity of data sources allows organizations to create a comprehensive view of their operations and market conditions. Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Alation

FEBRUARY 7, 2023

They sought documentation to help them locate the source of the data from the warehouse. The developers spent time looking for a tool that could scan all the SQL code and Microsoft SSIS packages because that was the ETL tool being used. Table and column lineage form an essential data foundation. It depends!

Data Warehouse 52

Data Warehouse ETL Business Intelligence Business Intelligence 52

DagsHub

OCTOBER 23, 2024

Data Preprocessing Here, you can process the unstructured data into a format that can be used for the other downstream tasks. For instance, if the collected data was a text document in the form of a PDF, the data preprocessing—or preparation stage —can extract tables from this document. Unstructured.io

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

JUNE 27, 2023

Using SQL-centric transformations to model data to be deployed. dbt is also great for data lineage and documentation to empower business analysts to make informed decisions on their data. Now we have one spot to check if the data is accurate. It is a compiler and a runner.

SQL 52

SQL Database Data Quality Data Warehouse 52

DagsHub

NOVEMBER 11, 2024

So, we must understand the different unstructured data types and effectively process them to uncover hidden patterns. Textual Data Textual data is one of the most common forms of unstructured data and can be in the format of documents, social media posts, emails, web pages, customer reviews, or conversation logs.

AI 52

AI AI Data Lakes Database 52

phData

SEPTEMBER 19, 2023

By incorporating metadata into the data model, users can easily discover, understand, and interpret the data stored in the lake. With the amounts of data involved, this can be crucial to utilizing a data lake effectively. Inaccurate or inconsistent data can undermine decision-making and erode trust in analytics.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

phData

SEPTEMBER 4, 2024

ThoughSpot can easily connect to top cloud data platforms such as Snowflake AI Data Cloud , Oracle, SAP HANA, and Google BigQuery. In that case, ThoughtSpot also leverages ELT/ETL tools and Mode, a code-first AI-powered data solution that gives data teams everything they need to go from raw data to the modern BI stack.

Analytics 52

Analytics Analytics SQL ETL 52

phData

FEBRUARY 22, 2023

Data Quality Good testing is an essential part of ensuring the integrity and reliability of data. Without testing, it is difficult to know whether the data is accurate, complete, and free of errors. Below, we will walk through some baseline tests every team could and should run to ensure data quality.

SQL 52

SQL Data Quality Database ETL 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

A 2019 survey by McKinsey on global data transformation revealed that 30 percent of total time spent by enterprise IT teams was spent on non-value-added tasks related to poor data quality and availability. It truly is an all-in-one data lake solution. Where should readers go to learn more about HPCC Systems?

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data. Read more here.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Mlearning.ai

JULY 10, 2023

The single most common way to create a view in a dataset is by CREATE VIEW DDL statement and you can refer to the official documentation to explore more options. You can use stored procedures to handle complex ETL processes, make API calls, and perform data validation.

SQL 52

SQL Database Apache Hadoop Data Science 52

phData

JUNE 7, 2023

Natural Language Understanding (NLU) : NLU is a subset of NLP focused on algorithms that can interpret the meaning of a sentence or document in terms of syntax, grammar, or ontology. You may think that AI is only for tech giants with massive budgets, colossal data sets, and a collection of proprietary technology.

AI 59

AI AI Machine Learning Machine Learning 59

The MLOps Blog

AUGUST 3, 2023

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. You have the function docstring because with procedural code generally in script form, there is no place to stick documentation naturally.

ML 52

ML ML Data Scientist Machine Learning 52

phData

AUGUST 17, 2023

They offer a range of features and integrations, so the choice depends on factors like the complexity of your data pipeline, requirements for connections to other services, user interface, and compatibility with any ETL software already in use. Proper error handling enhances the resilience and reliability of your data pipeline.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

phData

MAY 7, 2025

Whether youre a data engineer, architect, or platform owner, this approach can help you shift from reactive firefighting to proactive, intelligent data management. The ETL Pipeline failed in PROD. Trying to Level Up Your Data Platform with Iceberg?

DataOps 52

DataOps AWS SQL Data Engineering 52