Data Engineering Weekly

OCTOBER 27, 2024

Astasia Myers: The three components of the unstructured data stack LLMs and vector databases significantly improved the ability to process and understand unstructured data. The blog is an excellent summary of the existing unstructured data landscape. What are you waiting for?

Data Engineering 57

Data Engineering Data Engineer Engineering Unstructured Data 57

Data Engineering Podcast

NOVEMBER 27, 2022

Summary The data ecosystem has been growing rapidly, with new communities joining and bringing their preferred programming languages to the mix. This has led to inefficiencies in how data is stored, accessed, and shared across process and system boundaries. images, documents, etc.) images, documents, etc.)

Data Process 100

Data Process Process Metadata Business Intelligence 100

Snowflake

FEBRUARY 6, 2024

Diverse data provides a broader view and helps avoid the potential blinders traditional sources can perpetuate. To ensure your AI models are trained with as much data as possible, here are five best practices for greater data diversity: 1. Break down internal silos to access cross-functional sources.

Unstructured Data 105

Unstructured Data Retail Data Manufacturing 105

Snowflake

JUNE 28, 2023

At this year’s Summit, we are excited to announce a series of advancements to Snowpark runtimes and libraries, making the deployment and processing of non-SQL code in Snowflake even simpler, faster, and more secure. Snowpark — Set of libraries and runtimes for secure deployment and processing of non-SQL code on the Snowflake Data Cloud.

Python 52

Python Accessible Accessibility Pipeline-centric 52

Snowflake

NOVEMBER 4, 2024

Hear Dr. Andrew Ng talk about AI, agents and how to mobilize unstructured data Prominent AI researcher, founder of DeepLearning.AI Andrew Ng talk about AI, agents and how to mobilize unstructured data Prominent AI researcher, founder of DeepLearning.AI Does it have to change how your team works too?

Unstructured Data 86

Unstructured Data Python Data Pipeline Machine Learning 86

Snowflake

JULY 25, 2024

Snowflake Cortex Search, a fully managed search service for documents and other unstructured data, is now in public preview. Solving the challenges of building high-quality RAG applications From the beginning, Snowflake’s mission has been to empower customers to extract more value from their data.

Unstructured Data 111

Unstructured Data Metadata Government SQL 111

Data Engineering Podcast

SEPTEMBER 14, 2020

Tree Schema is a data catalog that is making metadata management accessible to everyone. With Tree Schema you can create your data catalog and have it fully populated in under five minutes when using one of the many automated adapters that can connect directly to your data stores.

Process 100

Process Unstructured Data Metadata Data Engineer 100

Rockset

APRIL 18, 2023

Organizations have continued to accumulate large quantities of unstructured data, ranging from text documents to multimedia content to machine and sensor data. Comprehending and understanding how to leverage unstructured data has remained challenging and costly, requiring technical depth and domain expertise.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

Cloudera

NOVEMBER 15, 2024

The manual process of switching between tools slows down their work, often leaving them reliant on rudimentary methods of keeping track of their findings. Unstructured data not ready for analysis: Even when defenders finally collect log data, it’s rarely in a format that’s ready for analysis.

Metadata 82

Metadata Unstructured Data Data Lake Government 82

phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. Efficiency through being able to streamline data storage and retrieval processes.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Knowledge Hut

JULY 24, 2023

The future of SQL (Structured Query Language) is a scalding subject among professionals in the data-driven world. As data generation continues to skyrocket, the demand for real-time decision-making, data processing, and analysis increases. It is also integrable with other programming languages like Python and R.

Database 52

Database SQL Process NoSQL 52

Monte Carlo

FEBRUARY 12, 2024

To differentiate and expand the usefulness of these models, organizations must augment them with first-party data – typically via a process called RAG (retrieval augmented generation). Today, this first-party data mostly lives in two types of data repositories. Quality : Is the data itself anomalous?

Database 72

Database Unstructured Data Data Pipeline Metadata 72

Snowflake

OCTOBER 26, 2023

But while the potential is theoretically limitless, there are a number of data challenges and risks HCLS executives need to be aware of when using AI that can create new content. Here’s how the right data strategy can help you get past the hazards and hurdles to implementing gen AI.

Healthcare 86

Healthcare Unstructured Data High Quality Data Medical 86

AltexSoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Process 69

Process Data Lake Metadata Data Warehouse 69

Snowflake

NOVEMBER 1, 2023

For organizations to fully capitalize on this potential, it’s critical that everyone — not just those with AI expertise — is able to access and use generative AI. With just a single line of SQL or Python, analysts can instantly access specialized ML and LLM models tuned for specific tasks. These functions include the ones listed below.

Unstructured Data 108

Unstructured Data SQL Python Accessible 108

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. Order of process phases. What is ELT?

Process 52

Process Building Raw Data Data Lake 52

Tweag

APRIL 19, 2023

The MedTech industry is buzzing thanks to a continuous stream of innovation, promising to be more precise, efficient and accessible than ever. To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. data import torch. But as it turns out, we can’t use it.

Medical 60

Medical Process Cloud Bytes 60

AltexSoft

OCTOBER 25, 2021

Its deep learning natural language processing algorithm is best in class for alleviating clinical documentation burnout, which is one of the main problems of healthcare technology. What is Natural Language Processing? This allows machines to extract value even from unstructured data. Nuance, acquired for $19.7

Medical 52

Medical Healthcare Process Hospitality 52

Data Engineering Weekly

JUNE 24, 2024

A few highlights from the report Unstructured data goes mainstream. link] Sponsored: 2024 State of Apache Airflow Report Gain access to the latest trends and insights shaping the world of Apache Airflow—the go-to platform for data pipeline development and orchestration. Are there enough usecases?

Data Engineering 109

Data Engineering Data Engineer Engineering Google Cloud 109

ProjectPro

NOVEMBER 30, 2021

In extract-transform-load (ETL), data is obtained from multiple sources, transformed, and stored in a single data warehouse, with access to data analysts , data scientists , and business analysts for data visualization and statistical analysis model building, forecasting, etc.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Cloudera

NOVEMBER 15, 2024

The manual process of switching between tools slows down their work, often leaving them reliant on rudimentary methods of keeping track of their findings. Unstructured data not ready for analysis: Even when defenders finally collect log data, it’s rarely in a format that’s ready for analysis.

Metadata 59

Metadata Unstructured Data Data Lake Government 59

phData: Data Engineering

NOVEMBER 8, 2024

Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms. These systems are built on open standards and offer immense analytical and transactional processing flexibility. Why should we use it?

Architecture 52

Architecture Systems Data Lake Google Cloud 52

Snowflake

SEPTEMBER 19, 2023

Why AI has everyone’s attention, what it means for different data roles, and how Alteryx and Snowflake are bringing AI to data use cases There’s a llama on the loose! With all the hoopla around AI, there’s a lot to get up to speed on—especially the implications this technology has for data analytics. Some takeaways?

Machine Learning 98

Machine Learning Unstructured Data Data Analytics Government 98

Snowflake

APRIL 10, 2023

Discover how Snowflake’s Data Cloud is helping semantic processing platform, semantha, reach its full potential—with Accenture’s support. semantha is a semantic processing platform that understands and processes human language—at astonishing scale and speed. From there, we can process information not unlike how humans do.

Unstructured Data 59

Unstructured Data Manufacturing Insurance Government 59

Striim

OCTOBER 11, 2024

Then, we’ll explore a data pipeline example and dive deeper into the key differences between a traditional data pipeline vs ETL. What is a Data Pipeline? A data pipeline refers to a series of processes that transport data from one or more sources to a destination, such as a data warehouse, database, or application.

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Data Pipeline MongoDB Unstructured Data Data Lake 52

Precisely

SEPTEMBER 6, 2023

To truly succeed in an increasingly data-driven world, organizations need data governance. Data governance is the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset. The same does not hold true for unstructured data.

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Data Governance Big Data Government Unstructured Data 52

Data Engineering Weekly

JULY 21, 2024

[link] Manuel Faysse: ColPali - Efficient Document Retrieval with Vision Language Models 👀 80% of enterprise data exists in difficult-to-use formats like HTML, PDF, CSV, PNG, PPTX, and more. ColPali’s approach integrates visual and textual cues more effectively, reducing indexing complexity and enhancing retrieval performance.

Data Engineering 117

Data Engineering Data Engineer Engineering Generalist 117

Snowflake

APRIL 8, 2024

BigGeo BigGeo accelerates geospatial data processing by optimizing performance and eliminating challenges typically associated with big data. The Innova-Q dashboard provides access to product safety and quality performance data, historical risk data, and analysis results for proactive risk management.

Pipeline-centric 128

Pipeline-centric Food Healthcare Unstructured Data 128

Snowflake

NOVEMBER 11, 2024

At BUILD 2024, we announced several enhancements and innovations designed to help you build and manage your data architecture on your terms. Ingest data more efficiently and manage costs For data managed by Snowflake, we are introducing features that help you access data easily and cost-effectively.

Data Architecture 82

Data Architecture Architecture Data Lake Kafka 82

Snowflake

NOVEMBER 2, 2023

Rather than defining schema upfront, a user can decide which data and schema they need for their use case. Snowflake has long supported semi-structured data types and file formats like JSON, XML, Parquet, and more recently storage and processing of unstructured data such as PDF documents, images, videos, and audio files.

Data Lake 94

Data Lake Data Warehouse Cloud Unstructured Data 94

Cloudera

AUGUST 21, 2024

Pockets and siloes of disparate data can accumulate across an enterprise or legacy data warehouses may not be equipped to properly manage a sea of structured and unstructured data at scale.

Unstructured Data 64

Unstructured Data Business Intelligence Data Architecture Data Warehouse 64

Knowledge Hut

APRIL 23, 2024

They also facilitate historical analysis, as they store long-term data records that can be used for trend analysis, forecasting, and decision-making. Big Data In contrast, big data encompasses the vast amounts of both structured and unstructured data that organizations generate on a daily basis.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Snowflake

JULY 9, 2024

Assess your current reality In recent strategy workshops with customers, we’ve focused on assessing four areas: data access, analytics and AI capabilities, organizational structure, and culture and communication. As we all know there is no AI strategy without a data strategy, so we start with the data.

Government 84

Government Education Data Governance Unstructured Data 84

Knowledge Hut

DECEMBER 21, 2023

This massive amount of data is referred to as “big data,” which comprises large amounts of data, including structured and unstructured data that has to be processed. To establish a career in big data, you need to be knowledgeable about some concepts, Hadoop being one of them. What is Hadoop?

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Monte Carlo

NOVEMBER 9, 2023

In my opinion, enterprise ready generative AI must be: Secure & private: Your AI application must ensure that your data is secure, private, and compliant, with proper access controls. We *know* what we’re putting in (raw, often unstructured data) and we *know* what we’re getting out, but we don’t know how it got there.

Unstructured Data 86

Unstructured Data Database Data Pipeline Architecture 86

Cloudera

SEPTEMBER 12, 2022

Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making. And second, for the data that is used, 80% is semi- or unstructured. Both obstacles can be overcome using modern data architectures, specifically data fabric and data lakehouse.

Unstructured Data 74

Unstructured Data Data Lake Data Architecture Data 74

Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake. What is the Difference Between Data Testing and Data Observability?

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Data Lake Data Pipeline Unstructured Data Datasets 98