Alation

JUNE 21, 2022

Why start with a data source and build a visualization, if you can just find a visualization that already exists, complete with metadata about it? Data scientists went beyond database tables to data lakes and cloud data stores. Data scientists want to catalog not just information sources, but models.

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DataOps Data Scientist Data Quality Data Pipeline 52

IBM Journey to AI blog

MAY 15, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. There are several styles of data integration. As a result, users boost pipeline performance while ensuring data security and controls.

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Data Pipeline ETL SQL Database 85

Alation

SEPTEMBER 7, 2021

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming.

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Data Governance Data Scientist Data Quality Data Profiling 59

AWS Machine Learning Blog

DECEMBER 1, 2023

Choose Amazon S3 as the data source and connect it to the dataset. After the dataset is loaded, create a data flow using that dataset. Switch to the analyses tab and create a Data Quality and Insights Report. This is a recommended step to analyze the quality of the input dataset.

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Machine Learning Machine Learning Data Preparation ML 102

Alation

AUGUST 11, 2022

Data mesh says architectures should be decentralized because there are inherent problems with centralized architectures. For example, when we centralize, all the focus goes on the data engineers. But there are only so many data engineers available in the market today; there’s a big skills shortage.

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phData

SEPTEMBER 20, 2023

Data mesh proposes a decentralized and domain-oriented model for data management to address these challenges. What are the Advantages and Disadvantages of Data Mesh? Advantages of Data Mesh Improved data quality due to domain teams having responsibility for their own data.

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Data Silos Database Data Quality Data Engineer 52

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. And it injects mature process control techniques from the world of traditional engineering. Take a look at figure 1 below.

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DataOps Data Pipeline Data Engineer Data Engineering 52

phData

JANUARY 24, 2024

Understanding Fivetran Fivetran is a user-friendly, code-free platform enabling customers to easily synchronize their data by automating extraction, transformation, and loading from many sources. Fivetran automates the time-consuming steps of the ELT process so your data engineers can focus on more impactful projects.

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Data Warehouse Data Pipeline Data Governance Cloud Data 52

Pickl AI

APRIL 9, 2025

The Top List of ETL Tools In the world of data management, Extract, Transform, Load (ETL) tools play a crucial role in enabling organizations to efficiently process and analyse large volumes of data. Apache NiFi Apache NiFi is an open-source data integration tool that provides an intuitive user interface for designing data flows.

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ETL Data Warehouse AWS Business Intelligence 52

phData

OCTOBER 17, 2023

In our previous blog , we discussed how Fivetran and dbt scale for any data volume and workload, both small and large. Now, you might be wondering what these tools can do for your data team and the efficiency of your organization as a whole. Can these tools help reduce the time our data engineers spend fixing things?

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ETL Data Pipeline Data Engineer Data Engineering 52

phData

OCTOBER 12, 2022

But maybe your business users want to be able to know if the data they’re consuming is fresh and up to their standards for data quality. dbt Cloud also gives your end users certainty that the data they’re using to make decisions is clean and current. Our team of data experts are happy to assist.

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SQL Data Warehouse Data Visualization Cloud Data 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. Read more here.

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Data Warehouse Analytics Analytics Cloud Data 52

The MLOps Blog

MAY 17, 2023

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. This ensures that the data which will be used for ML is accurate, reliable, and consistent.

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phData

MAY 6, 2024

Data Quality Next, dive into the details of your data. Another benefit of deterministic matching is that the process to build these identities is relatively simple, and tools your teams might already use, like SQL and dbt , can efficiently manage this process within your cloud data warehouse.

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Data Lakes Data Warehouse Cloud Data SQL 52

phData

SEPTEMBER 4, 2024

ThoughtSpot is a cloud-based AI-powered analytics platform that uses natural language processing (NLP) or natural language query (NLQ) to quickly query results and generate visualizations without the user needing to know any SQL or table relations. Suppose your business requires more robust capabilities across your technology stack.

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phData

JULY 23, 2024

However, certain considerations and cautions are required when working with a patient’s medical data. Data security is paramount to keeping patients’ data private, and data quality needs to be perfect to create an effective analysis. How can we improve clinical diagnoses? Why phData?

Machine Learning 52

Machine Learning Machine Learning Data Engineer Data Engineering 52

phData

SEPTEMBER 27, 2024

Data Quality Management : Persistent staging provides a clear demarcation between raw and processed customer data. This makes it easier to implement and manage data quality processes, ensuring your marketing efforts are based on clean, reliable data. Here’s where it gets really interesting.

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Data Modeling Data Models Apache Kafka Data Lakes 52

phData

NOVEMBER 1, 2024

It serves as a vital protective measure, ensuring proper data access while managing risks like data breaches and unauthorized use. Strong data governance also lays the foundation for better model performance, cost efficiency, and improved data quality, which directly contributes to regulatory compliance and more secure AI systems.

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