Smart Data Collective

APRIL 29, 2020

Redshift is the product for data warehousing, and Athena provides SQL data analytics. AWS Glue helps users to build data catalogues, and Quicksight provides data visualisation and dashboard construction. The services from AWS can be catered to meet the needs of each business user. Microsoft Azure.

Data Warehouse 99

Data Warehouse Azure SQL ETL 99

Smart Data Collective

SEPTEMBER 21, 2021

A data fabric solution must be capable of optimizing code natively using preferred programming languages in the data pipeline to be easily integrated into cloud platforms such as Amazon Web Services, Azure, Google Cloud, etc. This will enable the users to seamlessly work with code while developing data pipelines.

Data Quality 131

Data Quality Data Pipeline Database Internet of Things 131

Dataconomy

SEPTEMBER 4, 2023

Downtime, like the AWS outage in 2017 that affected several high-profile websites, can disrupt business operations. Understand what insights you need to gain from your data to drive business growth and strategy. Use ETL (Extract, Transform, Load) processes or data integration tools to streamline data ingestion.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Dataversity

JUNE 2, 2021

These models allow large enterprises to tier and scale their AWS Accounts, Azure Subscriptions, and Google Projects across hundreds and thousands of cloud users and services. The deliverability of cloud governance models has improved as public cloud usage continues to grow and mature. When we first started […].

Azure 52

Azure AWS Data Governance Data Quality 52

Pickl AI

OCTOBER 15, 2024

Talend Talend is a leading open-source ETL platform that offers comprehensive solutions for data integration, data quality , and cloud data management. It supports both batch and real-time data processing , making it highly versatile. ADF allows users to create complex ETL pipelines using a drag-and-drop interface.

ETL 52

ETL Azure AWS Data Governance 52

Pickl AI

MAY 15, 2024

Data Quality and Standardization The adage “garbage in, garbage out” holds true. Inconsistent data formats, missing values, and data bias can significantly impact the success of large-scale Data Science projects. This is crucial for building trust in models and addressing potential biases.

Data Analyst 52

Data Analyst Data Scientist Data Science Machine Learning 52

Pickl AI

NOVEMBER 4, 2024

Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. This process involves extracting data from multiple sources, transforming it into a consistent format, and loading it into the data warehouse. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

AUGUST 30, 2024

Assessment Evaluate the existing data quality and structure. This step involves identifying any data cleansing or transformation needed to ensure compatibility with the target system. Assessing data quality upfront can prevent issues later in the migration process.

Data Quality 52

Data Quality Data Governance Azure Database 52

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. Read Further: Azure Data Engineer Jobs.

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Pickl AI

MARCH 19, 2025

It allows users to design, automate, and monitor data flows, making it easier to handle complex data pipelines. Monte Carlo Monte Carlo is a data observability platform that helps engineers detect and resolve data quality issues. It is widely used for building efficient and scalable data pipelines.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

DECEMBER 25, 2024

Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Techniques such as data cleansing, aggregation, and trend analysis play a critical role in ensuring data quality and relevance.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

APRIL 21, 2025

Real-World Example: Healthcare systems manage a huge variety of data: structured patient demographics, semi-structured lab reports, and unstructured doctor’s notes, medical images (X-rays, MRIs), and even data from wearable health monitors. Ensuring data quality and accuracy is a major challenge.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

Alation

SEPTEMBER 30, 2022

Our ability to catalog every data asset means that we can partner with other ISVs in data quality and observability, like BigEye and Soda ; privacy, like BigID and OneTrust; access governance, like Immuta and Privacera; not to mention the core platforms, like Snowflake , Databricks , AWS , GCP, and Azure.

Data Governance 92

Data Governance Azure Data Quality SQL 92

Pickl AI

JULY 25, 2024

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. It provides a user-friendly interface for designing data flows.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

Pickl AI

OCTOBER 17, 2024

Best Practices for ETL Efficiency Maximising efficiency in ETL (Extract, Transform, Load) processes is crucial for organisations seeking to harness the power of data. Implementing best practices can improve performance, reduce costs, and improve data quality. Why is ETL Important for Businesses?

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

Pickl AI

APRIL 9, 2025

Integration : Can it connect with existing systems like AWS, Azure, or Google Cloud? Informatica PowerCenter Informatica PowerCenter is a leading enterprise-grade ETL tool known for its robust data integration capabilities. PowerCenter is particularly favored by large organizations with extensive data integration needs.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

Alation

NOVEMBER 11, 2021

Therefore, the question is not if a business should implement cloud data management and governance, but which framework is best for them. Whether you’re using a platform like AWS, Google Cloud, or Microsoft Azure, data governance is just as essential as it is for on-premises data. Data Quality Metrics.

Data Governance 52

Data Governance Cloud Data Data Quality Azure 52

phData

SEPTEMBER 19, 2023

Thankfully, there are tools available to help with metadata management, such as AWS Glue, Azure Data Catalog, or Alation, that can automate much of the process. What are the Best Data Modeling Methodologies and Processes? Data lakes are meant to be flexible for new incoming data, whether structured or unstructured.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Snorkel AI

OCTOBER 4, 2023

Snorkel offers enterprise-grade security in the SOC2-certified Snorkel Cloud , as well as partnerships with Google Cloud, Microsoft Azure, AWS, and other leading cloud providers. Snorkel’s data-centric approach and user-friendly platform can vastly simplify the training and deployment of credit-scoring models.

AI 64

AI AI ML ML 64

Precisely

MARCH 9, 2023

The same can be said of other leading platforms such as Databricks, Cloudera, and data lakes offered by the major cloud providers such as AWS, Google, and Microsoft Azure. Precisely helps enterprises manage the integrity of their data. Hadoop and Snowflake represent tremendous advances in analytics capabilities.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Big Data 59

Snorkel AI

OCTOBER 4, 2023

Snorkel offers enterprise-grade security in the SOC2-certified Snorkel Cloud , as well as partnerships with Google Cloud, Microsoft Azure, AWS, and other leading cloud providers. Snorkel’s data-centric approach and user-friendly platform can vastly simplify the training and deployment of credit-scoring models.

AI 59

AI AI ML ML 59

Pickl AI

JULY 12, 2024

This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models. Importance of Data in AI Quality data is the lifeblood of AI models, directly influencing their performance and reliability.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

JULY 25, 2023

Data Integration and ETL (Extract, Transform, Load) Data Engineers develop and manage data pipelines that extract data from various sources, transform it into a suitable format, and load it into the destination systems. Data Quality and Governance Ensuring data quality is a critical aspect of a Data Engineer’s role.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

The MLOps Blog

MAY 17, 2023

Here are some specific reasons why they are important: Data Integration: Organizations can integrate data from various sources using ETL pipelines. This provides data scientists with a unified view of the data and helps them decide how the model should be trained, values for hyperparameters, etc.

ETL 59

ETL Data Pipeline ML ML 59

Pickl AI

NOVEMBER 28, 2024

Cloud platforms like AWS , Google Cloud Platform (GCP), and Microsoft Azure provide managed services for Machine Learning, offering tools for model training, storage, and inference at scale. Scalability Considerations Scalability is a key concern in model deployment.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

OCTOBER 11, 2024

To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud , Fivetran, and Azure to automate invoice processing. This is where AI truly shines. phData’s Approach One of our largest financial services customers was struggling to keep up with the growing demand for invoice processing.

AI 52

AI AI Data Pipeline ML 52

O'Reilly Media

NOVEMBER 28, 2023

How will AI adopters react when the cost of renting infrastructure from AWS, Microsoft, or Google rises? Given the cost of equipping a data center with high-end GPUs, they probably won’t attempt to build their own infrastructure. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3%

AI 138

AI AI Data Analysis Data Analysis 138

Alation

DECEMBER 14, 2021

But by partnering with a professional consultant in data quality management systems, forward-thinking enterprises gain a significant competitive edge over their competitors. Amazon Web Services (AWS). Microsoft Azure. Let’s start with some simple definitions. What is cloud-native? Google Cloud Platform (GCP).

Cloud Computing 52

Cloud Computing Azure AWS Data Quality 52

Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Snorkel AI

OCTOBER 4, 2023

Snorkel offers enterprise-grade security in the SOC2-certified Snorkel Cloud , as well as partnerships with Google Cloud, Microsoft Azure, AWS, and other leading cloud providers. Snorkel’s data-centric approach and user-friendly platform can vastly simplify the training and deployment of credit-scoring models.

AI 52

AI AI ML ML 52

Dataconomy

APRIL 4, 2023

Scalability:  DaaS allows businesses to scale up or down their data usage based on their needs without having to worry about the cost of building, maintaining, and updating data sources.

Cloud Computing 79

Cloud Computing Internet of Things Azure Artificial Intelligence 79

Dataconomy

MARCH 13, 2023

Here are some of the essential tools and platforms that you need to consider: Cloud platforms Cloud platforms such as AWS , Google Cloud , and Microsoft Azure provide a range of services and tools that make it easier to develop, deploy, and manage AI applications. How to improve your data quality in four steps?

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Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm 98

Dataconomy

APRIL 4, 2023

Scalability:  DaaS allows businesses to scale up or down their data usage based on their needs without having to worry about the cost of building, maintaining, and updating data sources.

Cloud Computing 70

Cloud Computing Internet of Things Azure Artificial Intelligence 70

Pickl AI

APRIL 26, 2024

I break down the problem into smaller manageable tasks, define clear objectives, gather relevant data, apply appropriate analytical techniques, and iteratively refine the solution based on feedback and insights. Describe a situation where you had to think creatively to solve a data-related challenge.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

DagsHub

OCTOBER 23, 2024

They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52