Dataversity

MAY 26, 2023

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

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Dataversity

FEBRUARY 13, 2023

The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […] The post How to Assess Data Quality Readiness for Modern Data Pipelines appeared first on DATAVERSITY.

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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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Alation

MAY 31, 2023

The Data Governance & Information Quality Conference (DGIQ) is happening soon — and we’ll be onsite in San Diego from June 5-9. If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. The best part?

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Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. What’s worse, just 3% of the data in a business enterprise meets quality standards.

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Dataversity

JUNE 26, 2023

Today, businesses and individuals expect instant access to information and swift delivery of services. The same expectation applies to data, […] The post Leveraging Data Pipelines to Meet the Needs of the Business: Why the Speed of Data Matters appeared first on DATAVERSITY.

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Pickl AI

NOVEMBER 4, 2024

Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? They are crucial in ensuring data is readily available for analysis and reporting. from 2025 to 2030.

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Dataversity

NOVEMBER 20, 2024

Today’s data pipelines use transformations to convert raw data into meaningful insights. Yet, ensuring the accuracy and reliability of these transformations is no small feat – tools and methods to test the variety of data and transformation can be daunting.

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IBM Data Science in Practice

NOVEMBER 28, 2022

Implementing a data fabric architecture is the answer. What is a data fabric? Data fabric is defined by IBM as “an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems.”

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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. Data lineage helps during these investigations. How will one decision affect customers?

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Pickl AI

OCTOBER 11, 2023

It is the practice of monitoring, tracking, and ensuring data quality, reliability, and performance as it moves through an organization’s data pipelines and systems. Data quality tools help maintain high data quality standards. Tools Used in Data Observability?

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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. How Are Data Quality and Data Observability Similar—and How Are They Different?

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Alation

SEPTEMBER 22, 2022

This new partnership will unify governed, quality data into a single view, granting all stakeholders total visibility into pipelines and providing them with a superior ability to make data-driven decisions. For people to understand and trust data, they need to see it in context. Data Pipeline Strategy.

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phData

APRIL 23, 2024

Designing New Data Pipelines Takes a Considerable Amount of Time and Knowledge Designing new ingestion pipelines is a complex undertaking that demands significant time and expertise. Engineering teams must maintain a complex web of ingestion pipelines capable of supporting many different sources, each with its own intricacies.

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Pickl AI

OCTOBER 17, 2024

This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. Efficient management of ETL Data is essential for businesses seeking to leverage their information for strategic decision-making.

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Dataversity

MARCH 13, 2023

Companies are spending a lot of money on data and analytics capabilities, creating more and more data products for people inside and outside the company. These products rely on a tangle of data pipelines, each a choreography of software executions transporting data from one place to another.

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Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

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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. Everyone would be using the same data set to make informed decisions which may range from goal setting to prioritizing investments in sustainability.

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phData

AUGUST 17, 2023

Data pipeline orchestration tools are designed to automate and manage the execution of data pipelines. These tools help streamline and schedule data movement and processing tasks, ensuring efficient and reliable data flow. This enhances the reliability and resilience of the data pipeline.

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phData

AUGUST 5, 2024

Who should have access to sensitive data? How can my analysts discover where data is located? All of these questions describe a concept known as data governance. The Snowflake AI Data Cloud has built an entire blanket of features called Horizon, which tackles all of these questions and more.

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phData

OCTOBER 25, 2024

It’s common to have terabytes of data in most data warehouses, data quality monitoring is often challenging and cost-intensive due to dependencies on multiple tools and eventually ignored. This results in poor credibility and data consistency after some time, leading businesses to mistrust the data pipelines and processes.

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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? The value of data lineage applies across all industries, but there are three key focuses when you consider it for banking use cases: 1.

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Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Big data analytics from 2022 show a dramatic surge in information consumption.

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Alation

APRIL 18, 2023

This, in turn, helps them to build new data pipelines, solutions, and products, or clean up the data that’s there. It bears mentioning data profiling has evolved tremendously. In the past, experts would need to write dozens of queries to extract this information over hours or days.

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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.

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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. But the implementation of AI is only one piece of the puzzle. And that makes sense.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Focused on addressing the challenge of agricultural data standardization, Agmatix has developed proprietary patented technology to harmonize and standardize data, facilitating informed decision-making in agriculture. The first step in developing and deploying generative AI use cases is having a well-defined data strategy.

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Alation

DECEMBER 7, 2021

Do we have end-to-end data pipeline control? What can we learn about our data quality issues? How can we improve and deliver trusted data to the organization? One major obstacle presented to data quality is data silos , as they obstruct transparency and make collaboration tough. Unified Teams.

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Precisely

MAY 4, 2023

quintillion exabytes of data every day. That information resides in multiple systems, including legacy on-premises systems, cloud applications, and hybrid environments. It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation.

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IBM Journey to AI blog

DECEMBER 16, 2022

So, instead of wandering the aisles in hopes you’ll stumble across the book, you can walk straight to it and get the information you want much faster. An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more.

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DagsHub

AUGUST 23, 2024

As a result, Gartner estimates that poor data quality costs organizations an average of $13 million annually. High-quality data significantly reduces the risk of costly errors, and the resulting penalties or legal issues. Data Governance Data governance is the foundation of any data quality framework.

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Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that. Data engineers want to catalog data pipelines.

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Precisely

MAY 13, 2024

A well-integrated data catalog supports the extensive discovery, access, use, and sharing of both technical and business metadata – automating and improving your data integration and operations tasks. Europe’s GDPR and similar legislation worldwide underscore the vital importance of data governance.

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Precisely

MAY 23, 2024

The sudden popularity of cloud data platforms like Databricks , Snowflake , Amazon Redshift, Amazon RDS, Confluent Cloud , and Azure Synapse has accelerated the need for powerful data integration tools that can deliver large volumes of information from transactional applications to the cloud reliably, at scale, and in real time.

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DagsHub

OCTOBER 23, 2024

How to leverage Generative AI to manage unstructured data Benefits of applying proper unstructured data management processes to your AI/ML project. What is Unstructured Data? One thing is clear : unstructured data doesn’t mean it lacks information.

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Pickl AI

OCTOBER 15, 2024

These platforms extract data from various sources, transform it into usable formats, and load it into target systems. The right ETL platform ensures data flows seamlessly across systems, providing accurate and consistent information for decision-making.

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Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. Introduction The ETL process is crucial in modern data management.

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