Dataconomy

FEBRUARY 12, 2024

Moreover, data integration platforms are emerging as crucial orchestrators, simplifying intricate data pipelines and facilitating seamless connectivity across disparate systems and data sources. These platforms provide a unified view of data, enabling businesses to derive insights from diverse datasets efficiently.

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Alation

MAY 16, 2023

But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The answer?

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AWS Machine Learning Blog

DECEMBER 18, 2024

All data generation and processing steps were run in parallel directly on the SageMaker HyperPod cluster nodes, using a unique working environment and highlighting the clusters versatility for various tasks beyond just training models. She specializes in AI operations, data governance, and cloud architecture on AWS.

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IBM Journey to AI blog

FEBRUARY 26, 2024

The financial services industry has been in the process of modernizing its data governance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. That’s why data pipeline observability is so important.

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IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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IBM Journey to AI blog

APRIL 10, 2023

To further the above, organizations should have the right foundation that consists of a modern data governance approach and data architecture. It’s becoming critical that organizations should adopt a data architecture that supports AI governance.

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Precisely

SEPTEMBER 30, 2024

Data enrichment adds context to existing information, enabling business leaders to draw valuable new insights that would otherwise not have been possible. Managing an increasingly complex array of data sources requires a disciplined approach to integration, API management, and data security.

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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. And that makes sense.

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ODSC - Open Data Science

JUNE 19, 2023

There is no doubt that real-time operating systems (RTOS) have an important role in the future of big data collection and processing. How does RTOS help advance big data processing? In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible.

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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

JANUARY 17, 2023

IBM Cloud Pak for Data Express solutions provide new clients with affordable and high impact capabilities to expeditiously explore and validate the path to become a data-driven enterprise. IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture.

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IBM Journey to AI blog

APRIL 20, 2023

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

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IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

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ODSC - Open Data Science

MAY 26, 2025

Itneeds: Scalable cloud infrastructure Clean, structured data pipelines Skilled professionals who can fine-tune and monitormodels Cutting corners leads to short-term projects that fizzle out. This includes not just the initial cost of AI tools, but the ongoing investment in infrastructure, talent, and integration.

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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. Read more to know. Cloud Platforms: AWS, Azure, Google Cloud, etc.

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Precisely

MAY 13, 2024

If you’re in the market for a data integration solution, there are many things to consider – including the flexibility of integration solutions, the availability of a strong network of service providers, and the vendor’s reputation for thought leadership in the integration space.

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IBM Journey to AI blog

JANUARY 9, 2024

The groundwork of training data in an AI model is comparable to piloting an airplane. The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. This may also entail working with new data through methods like web scraping or uploading.

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Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

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phData

FEBRUARY 14, 2023

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

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Precisely

MAY 23, 2024

Artificial intelligence (AI) algorithms are trained to detect anomalies. Integrated data catalog for metadata support As you build out your IT ecosystem, it is important to leverage tools that have the capabilities to support forward-looking use cases. A notable capability that achieves this is the data catalog.

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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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ODSC - Open Data Science

FEBRUARY 5, 2025

Both co-located summits, Generative AI X and Data Engineering will run on Wednesday, offering attendees a chance to delve into specialized topics. At the Data Engineering Summit, experts will cover data pipelines, real-time processing, and best practices for scalable data infrastructures.

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Pickl AI

DECEMBER 11, 2024

Focusing only on what truly matters reduces data clutter, enhances decision-making, and improves the speed at which actionable insights are generated. Streamlined Data Pipelines Efficient data pipelines form the backbone of lean data management.

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Precisely

AUGUST 15, 2024

Create an AI-driven data and process improvement loop to continuously enhance your business operations. An increasing number of GenAI tools use large language models that automate key data engineering, governance, and master data management tasks. Let’s dive deeper into data readiness next.

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Precisely

MAY 23, 2024

Artificial intelligence (AI) algorithms are trained to detect anomalies. Integrated data catalog for metadata support As you build out your IT ecosystem, it is important to leverage tools that have the capabilities to support forward-looking use cases. A notable capability that achieves this is the data catalog.

Data Governance 52

Data Governance ETL Data Pipeline Azure 52

phData

APRIL 18, 2023

Snowflake enables organizations to instantaneously scale to meet SLAs with timely delivery of regulatory obligations like SEC Filings, MiFID II, Dodd-Frank, FRTB, or Basel III—all with a single copy of data enabled by data sharing capabilities across various internal departments.

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ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

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ODSC - Open Data Science

DECEMBER 3, 2024

While the concept of data mesh as a data architecture model has been around for a while, it was hard to define how to implement it easily and at scale. Two data catalogs went open-source this year, changing how companies manage their data pipeline. The departments closest to data should own it.

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Dataversity

APRIL 30, 2021

The goal of digital transformation remains the same as ever – to become more data-driven. We have learned how to gain a competitive advantage by capturing business events in data. Events are data snap-shots of complex activity sourced from the web, customer systems, ERP transactions, social media, […].

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Pickl AI

OCTOBER 15, 2024

Apache Nifi Apache Nifi is an open-source ETL tool that automates data flow between systems. It is well known for its data provenance and seamless data routing capabilities. Nifi provides a graphical interface for designing data pipelines , allowing users to track data flows in real-time.

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Pickl AI

OCTOBER 10, 2024

Support for Advanced Analytics : Transformed data is ready for use in Advanced Analytics, Machine Learning, and Business Intelligence applications, driving better decision-making. Using data transformation tools is key to staying competitive in a data-driven world, offering both efficiency and reliability.

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ODSC - Open Data Science

APRIL 26, 2023

Data Engineer Data engineers are the authors of the infrastructure that stores, processes, and manages the large volumes of data an organization has. The main aspect of their profession is the building and maintenance of data pipelines, which allow for data to move between sources.

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Alation

JANUARY 17, 2023

Data literacy — Employees can interpret and analyze data to draw logical conclusions; they can also identify subject matter experts best equipped to educate on specific data assets. Data governance is a key use case of the modern data stack. Who Can Adopt the Modern Data Stack?

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Pickl AI

OCTOBER 17, 2024

Apache NiFi As an open-source data integration tool, Apache NiFi enables seamless data flow and transformation across systems. Its drag-and-drop interface simplifies the design of data pipelines, making it easier for users to implement complex transformation logic.

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Snorkel AI

MAY 12, 2023

The magic behind these experiences is most often attributed to artificial intelligence and machine learning. That’s data. The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications.

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Snorkel AI

MAY 12, 2023

The magic behind these experiences is most often attributed to artificial intelligence and machine learning. That’s data. The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

JULY 18, 2023

Better Transparency: There’s more clarity about where data is coming from, where it’s going, why it’s being transformed, and how it’s being used. Improved Data Governance: This level of transparency can also enhance data governance and control mechanisms in the new data system.

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