Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. What is Data Engineering? million by 2028.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

OCTOBER 10, 2023

In today’s fast-paced business environment, the significance of Data Observability cannot be overstated. Data Observability enables organizations to detect anomalies, troubleshoot issues, and maintain data pipelines effectively. Schema A data schema defines the structure and organization of your data.

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IBM Journey to AI blog

OCTOBER 12, 2023

Securing AI models and their access to data While AI models need flexibility to access data across a hybrid infrastructure, they also need safeguarding from tampering (unintentional or otherwise) and, especially, protected access to data. Bias can also find its way into a model’s outputs long after deployment.

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AI AI Data Scientist Data Governance 93

Alation

DECEMBER 23, 2021

The elf teams used data engineering to improve gift matching and deployed big data to scale the naughty and nice list long ago , before either approach was even considered within our warmer climes. The best data was discovered, experts were identified, and conversations were starting. Make data governance an asset.

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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The MLOps Blog

JUNE 27, 2023

A self-service infrastructure portal for infrastructure and governance. Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. It could help you detect and prevent data pipeline failures, data drift, and anomalies.

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DataRobot Blog

OCTOBER 3, 2017

With this integration, customers can now harness the full power of Azure’s Big Data offerings in a self-service manner to gain immediate value.”. This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance.

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Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Learn more about designing the right data architecture to elevate your data quality here.

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Data Quality Data Profiling Data Governance Analytics 74

Pickl AI

OCTOBER 10, 2024

Enhanced Data Quality : These tools ensure data consistency and accuracy, eliminating errors often occurring during manual transformation. Scalability : Whether handling small datasets or processing big data, transformation tools can easily scale to accommodate growing data volumes.

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Pickl AI

JULY 30, 2024

Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation. With a user-friendly interface and robust features, NiFi simplifies complex data workflows and enhances real-time data integration.

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Precisely

MAY 13, 2024

It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format. Questions to ask each vendor: Does your data integration solution provide access to the metadata? What data governance controls do your solutions have in place?

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Data Governance Data Pipeline Cloud Data Data Quality 52

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Data Quality Data Lakes Data Warehouse Big Data 52

IBM Journey to AI blog

DECEMBER 16, 2022

And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.

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Data Quality Data Governance Data Scientist Data Wrangling 52

DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. It primarily caters to large organizations with complex data environments.

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Data Quality Data Governance Machine Learning Machine Learning 52

Alation

JANUARY 17, 2023

Data ingestion/integration services. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? What Are the Benefits of a Modern Data Stack?

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Data Warehouse ETL Tableau Cloud Data 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.

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Machine Learning Machine Learning Data Lakes AI 52

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

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Snorkel AI

MAY 12, 2023

Compute, big data, large commoditized models—all important stages. But now we’re entering a period where data investments have massive returns from all performance as well as business impact. The reason is that most teams do not have access to a robust data ecosystem for ML development.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

Compute, big data, large commoditized models—all important stages. But now we’re entering a period where data investments have massive returns from all performance as well as business impact. The reason is that most teams do not have access to a robust data ecosystem for ML development.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataversity

MARCH 3, 2023

Have you ever waited for that one expensive parcel that shows “shipped,” but you have no clue where it is? The tracking history stopped updating five days ago, and you have almost lost hope. But wait, 11 days later, you have it at your doorstep.

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Data Observability Data Pipeline Data Governance Big Data 52

Dataversity

MARCH 3, 2023

Have you ever waited for that one expensive parcel that shows “shipped,” but you have no clue where it is? The tracking history stopped updating five days ago, and you have almost lost hope. But wait, 11 days later, you have it at your doorstep.

Data Observability 52

Data Observability Data Pipeline Data Governance Big Data 52

Flipboard

MARCH 21, 2025

To establish trust between the data producers and data consumers, SageMaker Catalog also integrates the data quality metrics and data lineage events to track and drive transparency in data pipelines. Data analysts discover the data and subscribe to the data.

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SQL Data Analyst Data Warehouse AWS 142

Data Science Dojo

APRIL 3, 2025

Securiti’s Data Command Graph delivers this foundation by providing comprehensive contextual insights about data objects and their controls, enabling complete monitoring and governance of the entire enterprise AI system across all interconnected components rather than focusing solely on models.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

The first step in developing and deploying generative AI use cases is having a well-defined data strategy. Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer.

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