Dataconomy

OCTOBER 8, 2024

Accurate and secure data can help to streamline software engineering processes and lead to the creation of more powerful AI tools, but it has become a challenge to maintain the quality of the expansive volumes of data needed by the most advanced AI models. Featured image credit: Shubham Dhage/Unsplash

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Precisely

DECEMBER 28, 2023

Many scenarios call for up-to-the-minute information. Enterprise technology is having a watershed moment; no longer do we access information once a week, or even once a day. Now, information is dynamic. Business success is based on how we use continuously changing data. What is a streaming data pipeline?

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Data Science Dojo

SEPTEMBER 11, 2024

Let’s explore each of these components and its application in the sales domain: Synapse Data Engineering: Synapse Data Engineering provides a powerful Spark platform designed for large-scale data transformations through Lakehouse. Here, we changed the data types of columns and dealt with missing values.

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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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Towards AI

OCTOBER 31, 2024

This approach not only enhances data diversity but also alleviates privacy concerns related to sensitive patient data. Image by author This approach not only increases data diversity but also addresses privacy concerns related to sharing sensitive patient information. Example prompt use case #3.

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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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Dataversity

SEPTEMBER 6, 2024

Data pipelines are like insurance. ETL processes are constantly toiling away behind the scenes, doing heavy lifting to connect the sources of data from the real world with the warehouses and lakes that make the data useful. You only know they exist when something goes wrong.

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Dataconomy

FEBRUARY 25, 2025

Establishing the foundation for scalable data pipelines Initiating the process of creating scalable data pipelines requires addressing common challenges such as data fragmentation, inconsistent quality and siloed team operations.

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Dataversity

DECEMBER 6, 2023

Data pipelines are a set of processes that move data from one place to another, typically from the source of data to a storage system. These processes involve data extraction from various sources, transformation to fit business or technical needs, and loading into a final destination for analysis or reporting.

Data Pipeline 59

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Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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Dataversity

DECEMBER 19, 2023

In part one of this blog post, we described why there are many challenges for developers of data pipeline testing tools (complexities of technologies, large variety of data structures and formats, and the need to support diverse CI/CD pipelines).

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Flipboard

FEBRUARY 3, 2023

Over the last few years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a noteworthy piece of day-to-day business New-age technologies are almost entirely running the world today. Among these technologies, big data has gained significant traction. This concept is …

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Women in Big Data

FEBRUARY 27, 2025

Big data pipelines are the backbone of modern data processing, enabling organizations to collect, process, and analyze vast amounts of data in real-time. Issues such as data inconsistencies, performance bottlenecks, and failures are inevitable.In Validate data format and schema compatibility.

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Flipboard

NOVEMBER 7, 2023

“Data is at the center of every application, process, and business decision,” wrote Swami Sivasubramanian, VP of Database, Analytics, and Machine Learning at AWS, and I couldn’t agree more. A common pattern customers use today is to build data pipelines to move data from Amazon Aurora to Amazon Redshift.

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Dataconomy

MARCH 26, 2025

ETL pipelines are revolutionizing the way organizations manage data by transforming raw information into valuable insights. They serve as the backbone of data-driven decision-making, allowing businesses to harness the power of their data through a structured process that includes extraction, transformation, and loading.

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Dataversity

NOVEMBER 15, 2023

Those who want to design universal data pipelines and ETL testing tools face a tough challenge because of the vastness and variety of technologies: Each data pipeline platform embodies a unique philosophy, architectural design, and set of operations.

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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

APRIL 7, 2025

Graceful External Termination: Handling Pod Deletions in Kubernetes Data Ingestion and Streaming Jobs When running big-data pipelines in Kubernetes, especially streaming jobs, its easy to overlook how these jobs deal with termination. If not handled correctly, this can lead to locks, data issues, and a negative user experience.

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IBM Data Science in Practice

JANUARY 13, 2025

The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines. Background One of the Analytics teams tasks is to load data from multiple sources and unify it into a data warehouse.

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phData

AUGUST 6, 2024

As today’s world keeps progressing towards data-driven decisions, organizations must have quality data created from efficient and effective data pipelines. For customers in Snowflake, Snowpark is a powerful tool for building these effective and scalable data pipelines.

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Data Science Dojo

JULY 5, 2023

The development of a Machine Learning Model can be divided into three main stages: Building your ML data pipeline: This stage involves gathering data, cleaning it, and preparing it for modeling. This information can be used to inform the design of the model.

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Flipboard

MAY 16, 2025

While speaking at AIMs event DES 2025, Manjunatha G, engineering and site leader at the 3M Global Technology Centre, laid out a practical path to integrate AI agents into data engineering workflows.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

While these models are trained on vast amounts of generic data, they often lack the organization-specific context and up-to-date information needed for accurate responses in business settings. After ingesting the data, you create an agent with specific instructions: agent_instruction = """You are the Amazon Bedrock Agent.

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Flipboard

NOVEMBER 27, 2024

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. Port: Redshift 5439. Database name: dev.

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PyImageSearch

JANUARY 15, 2024

Home Table of Contents Adversarial Learning with Keras and TensorFlow (Part 2): Implementing the Neural Structured Learning (NSL) Framework and Building a Data Pipeline Adversarial Learning with NSL CIFAR-10 Dataset Configuring Your Development Environment Need Help Configuring Your Development Environment? We open our config.py

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Dataconomy

MARCH 11, 2025

Understanding the purpose of complex event processing CEP serves to monitor vast data streams from diverse sources, including but not limited to sensors, social media, and financial markets, facilitating enhanced decision-making. Real-time data management The importance of real-time data in todays analytics landscape cannot be overstated.

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Flipboard

MARCH 3, 2023

Data proliferation has become a norm and as organizations become more data driven, automating data pipelines that enable data ingestion, curation, …

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IBM Data Science in Practice

DECEMBER 7, 2022

One of the key elements that builds a data fabric architecture is to weave integrated data from many different sources, transform and enrich data, and deliver it to downstream data consumers. As a part of data pipeline, Address Verification Interface (AVI) can remediate bad address data.

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AWS Machine Learning Blog

APRIL 3, 2025

Data is one of the most critical assets of many organizations. Theyre constantly seeking ways to use their vast amounts of information to gain competitive advantages. This enables OMRON to extract meaningful patterns and trends from its vast data repositories, supporting more informed decision-making at all levels of the organization.

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Data Science Blog

FEBRUARY 4, 2023

ERP (Enterprise Resource Planning) systems contain information about finance, supplier management, human resources and other operational processes, while CRM (Customer Relationship Management) systems provide data about customer relationships, marketing and sales activities. Copyright by DATANOMIQ.

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Flipboard

JUNE 3, 2025

Traditional systems lack the capability to efficiently process vast amounts of real-time and historical data or provide personalized, station-level recommendations. This limits operators ability to make timely, informed decisionsresulting in higher electricity costs, underutilized assets, and a subpar customer experience.

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Precisely

JANUARY 6, 2025

See how Preciselys Data Integration Solutions can help your business stream real-time application data from legacy systems to mission critical business applications and analytics platforms. These architectures prioritize: Real-time data availability: Ensuring that data is accessible and actionable the moment it is generated.

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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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Smart Data Collective

SEPTEMBER 23, 2020

Unstructured data that has been cleared to suit a plan, sort out into tables, and defined by relationships and types, is known as structured data. This is a vital disparity between data warehouses and data lakes. Data warehouses contain historical information that has been cleared to suit a relational plan.

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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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Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. What Does a Data Engineer Do?

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Data Science Dojo

AUGUST 11, 2023

Throughout the course of history, the significance of creating and disseminating information has been immensely crucial. By applying statistical concepts such as central tendency, variability, and correlation, data scientists can gain insights into the underlying structure of data.

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