Data Engineering Weekly

MARCH 5, 2025

Data Silos: Breaking down barriers between data sources. Hadoop achieved this through distributed processing and storage, using a framework called MapReduce and the Hadoop Distributed File System (HDFS). Start the Data Governance Process: Don't wait until the last minute to build the data governance framework.

Hadoop 57

Hadoop Metadata Data Ingestion Data Governance 57

Data Engineering Weekly

FEBRUARY 9, 2025

I found the product blog from QuantumBlack gives a view of data quality in unstructured data. link] Pinterest: Advancements in Embedding-Based Retrieval at Pinterest Homefeed Pinterest writes about its embedding-based retrieval system enhancements for Homefeed personalization and engagement.

Data Engineering 57

Data Engineering Data Engineer Engineering Unstructured Data 57

AltexSoft

JULY 27, 2021

You’ll learn about the types of recommender systems, their differences, strengths, weaknesses, and real-life examples. Personalization and recommender systems in a nutshell. Primarily developed to help users deal with a large range of choices they encounter, recommender systems come into play. Amazon, Booking.com) and.

Machine Learning 76

Machine Learning Systems Algorithm Deep Learning 76

Snowflake

MARCH 14, 2024

Along with SNP Glue, the Snowflake Native App gives customers a simple, flexible and cost-effective solution to get data out of SAP and into Snowflake quickly and accurately. What’s the challenge with unlocking SAP data? Getting direct access to SAP data is critical because it holds such a breadth of ERP information.

IT 117

IT Data Ingestion Data AWS 117

Striim

SEPTEMBER 11, 2024

Understanding the essential components of data pipelines is crucial for designing efficient and effective data architectures. Here are six key components that are fundamental to building and maintaining an effective data pipeline. It offers scalable and high-performance tools that enable efficient data access and utilization.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Snowflake

MARCH 30, 2023

For this reason, a new data management for ML framework has emerged to help manage this complexity: the “feature store.” Feature store As described in Tecton’s blog , a feature store is a data management system for managing ML feature pipelines, including the management of feature engineering code and data.

Engineering 104

Engineering Raw Data Data Science Machine Learning 104

Snowflake

JUNE 5, 2024

It provides access to industry-leading large language models (LLMs), enabling users to easily build and deploy AI-powered applications. By using Cortex, enterprises can bring AI directly to the governed data to quickly extend access and governance policies to the models.

Coding 111

Coding Building Management Government 111

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 114

Data Lake Data Warehouse Cloud Unstructured Data 114

Data Engineering Podcast

FEBRUARY 3, 2019

So I decided to focus my energies in research data management. Open Context is an open access data publishing service for archaeology. It started because we need better ways of dissminating structured data and digital media than is possible with conventional articles, books and reports.

Digital Media 100

Digital Media Media PostgreSQL Datasets 100

AltexSoft

JUNE 7, 2021

As a result, a Big Data analytics task is split up, with each machine performing its own little part in parallel. Hadoop hides away the complexities of distributed computing, offering an abstracted API to get direct access to the system’s functionality and its benefits — such as. A file stored in the system ?an’t

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Monte Carlo

MARCH 12, 2025

For example, when theres an issue, only the ML, BE, or engineers have access to the AI stack, system, and logs to understand the issue, and only the data scientists have the expertise to actually solve it. With that expansion comes new challenges and new learning opportunities when it comes to GenAI development.

AWS 52

AWS Google Cloud Unstructured Data Coding 52

Monte Carlo

MARCH 12, 2025

For example, when theres an issue, only the ML, BE, or engineers have access to the AI stack, system, and logs to understand the issue, and only the data scientists have the expertise to actually solve it. With that expansion comes new challenges and new learning opportunities when it comes to GenAI development.

AWS 52

AWS Google Cloud Unstructured Data Coding 52

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 112

IT Unstructured Data Data Architecture Government 112

ThoughtSpot

MARCH 5, 2024

Create Snowflake dynamic tables In Snowflake, create dynamic tables by writing SQL queries that define how data should be transformed and materialized. Grant ThoughtSpot access In Snowflake, grant the ThoughtSpot service account USAGE privileges on the schemas containing the dynamic tables. Set refresh schedules as needed.

BI 111

BI Datasets SQL Raw Data 111

Monte Carlo

JUNE 6, 2023

Now, let’s take a closer look at the strengths and weaknesses of the most popular data quality team structures. Data engineering Having the data engineering team lead the response to data quality is by far the most common pattern. It is deployed by about half of all organizations that use a modern data stack.

Data Governance 98

Data Governance Government Data Data Engineering 98

Towards Data Science

JUNE 8, 2023

Now, let’s take a closer look at the strengths and weaknesses of the most popular data quality team structures. Data engineering Photo by Luke Chesser on Unsplash Having the data engineering team lead the response to data quality is by far the most common pattern. There are downsides to this approach however.

Data Governance 98

Data Governance Government Generalist Data Engineering 98

Cloudera

DECEMBER 11, 2020

As mentioned in my previous blog on the topic , the recent shift to remote working has seen an increase in conversations around how data is managed. Toolsets and strategies have had to shift to ensure controlled access to data. Driving innovation with secure and governed data .

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

Confluent

OCTOBER 3, 2019

We recently launched a new artificial intelligence (AI) data extraction API called Scrapinghub AutoExtract , which turns article and product pages into structured data. At Scrapinghub, we specialize in web data extraction , and our products empower everyone from programmers to CEOs to extract web data quickly and effectively.

Kafka 16

Kafka Cloud Amazon Web Services Google Cloud 16

Cloudera

SEPTEMBER 17, 2020

By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. . This led them to fall behind.

Data Warehouse 81

Data Warehouse Unstructured Data Pharmaceutical MySQL 81

U-Next

MARCH 1, 2023

They build scalable data processing pipelines and provide analytical insights to business users. A Data Engineer also designs, builds, integrates, and manages large-scale data processing systems. It’s not just the data itself that is important, but also how that data can be used to make better decisions.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Towards Data Science

MAY 11, 2023

Sharvit deconstructs the elements of complexity that sometimes seems inevitable with OOP and summarizes the main principles of DOP that helps us make the system more manageable. As its name suggests, DOP puts data first and foremost. to control who can access/change data in Python. These principles are language-agnostic.

Programming 98

Programming Python Data Schemas Java 98

Edureka

JUNE 7, 2024

paintings, songs, code) Historical data relevant to the prediction task (e.g., Unlike traditional AI systems that operate on pre-existing data, generative AI models learn the underlying patterns and relationships within their training data and use that knowledge to create novel outputs that did not previously exist.

Deep Learning 114

Deep Learning Media Manufacturing Algorithm 114

Cloudera

JULY 26, 2021

In fact, data product development introduces an additional requirement that wasn’t as relevant in the past as it is today: That of scalability in permissioning and authorization given the number and multitude of different roles of data constituents, both internal and external accessing a data product.

Generalist 119

Generalist Telecommunication Healthcare Data Science 119

Knowledge Hut

FEBRUARY 29, 2024

A database is a structured data collection that is stored and accessed electronically. File systems can store small datasets, while computer clusters or cloud storage keeps larger datasets. According to a database model, the organization of data is known as database design.

Data Science 98

Data Science Datasets Machine Learning Database Design 98

Knowledge Hut

DECEMBER 26, 2023

According to the Cybercrime Magazine, the global data storage is projected to be 200+ zettabytes (1 zettabyte = 10 12 gigabytes) by 2025, including the data stored on the cloud, personal devices, and public and private IT infrastructures. Data Analyst Scientist.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

Edureka

FEBRUARY 12, 2025

Flexibility and Modularity : The modular design of LangChain lets coders change how parts work, connect them to other systems, and try out different setups. External API Calls LLMs can talk to APIs to get data in real time, do calculations, or connect to outside systems like databases and search engines. How does LangChain work?

IT 52

IT Database Google Cloud Coding 52

Cloudera

SEPTEMBER 29, 2021

Among governments’ priorities are encouraging digital adoption, facilitating access and usage of relevant government services alongside enabling more digital transactions. Among the use cases for the government organizations that we are working on is one which leverages machine learning to detect fraud in payment systems nationwide.

Medical 83

Medical Government Hospitality Electronics 83

Snowflake

AUGUST 25, 2023

Our Code Llama fine-tuned (7b, 34b) for text-to-SQL outperforms base Code Llama (7b, 34b) by 16 and 9 percent-accuracy points respectively Evaluating performance of SQL-generation models Performance of our text-to-SQL models is reported against the “dev” subset of the Spider data set.

Coding 96

Coding SQL Data Cleanse Database 96

Confluent

JUNE 13, 2019

This data pipeline is a great example of a use case for Apache Kafka ®. Observational astronomers study many different types of objects, from asteroids in our own solar system to galaxies that are billions of lightyears away. The technology underlying the ZTF system should be a prototype that reliably scales to LSST needs.

Kafka 102

Kafka Bytes Python Data Pipeline 102

Netflix Tech

DECEMBER 14, 2018

In the previous blog posts in this series, we introduced the N etflix M edia D ata B ase ( NMDB ) and its salient “Media Document” data model. In this post we will provide details of the NMDB system architecture beginning with the system requirements?—?these key value stores generally allow storing any data under a key).

Media 97

Media Database Metadata Data Schemas 97

Data Engineering Weekly

MAY 5, 2024

The motivation for Machine Unlearning is critical from the privacy perspective and for model correction, fixing outdated knowledge, and access revocation of the training dataset. link] LinkedIn: LakeChime - A Data Trigger Service for Modern Data Lakes LinkedIn points out two critical flaws in a partitioned approach to data management.

Data Engineering 67

Data Engineering Data Engineer Engineering Google Cloud 67

Netflix Tech

MARCH 5, 2019

This operational component places some cognitive load on our engineers, requiring them to develop deep understanding of telemetry and alerting systems, capacity provisioning process, security and reliability best practices, and a vast amount of informal knowledge about the cloud infrastructure.

Cloud 74

Cloud Building Amazon Web Services Metadata 74

Engineering at Meta

MARCH 18, 2024

Systems and application logs play a key role in operations, observability, and debugging workflows at Meta. We designed the system to support service-level guarantees on log freshness, completeness, durability, query latency, and query result completeness. PyTorch, data readers, checkpointing, framework code, and hardware).

Engineering 94

Engineering Metadata Architecture Designing 94

U-Next

SEPTEMBER 7, 2022

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? . Built to make strategic use of data, a Data Warehouse is a combination of technologies and components. Data Warehouse in DBMS: .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). While functional, our current setup for managing tables is fragmented.

Big Data 78

Big Data Data Management Management Metadata 78

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52