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The motivation for Machine Unlearning is critical from the privacy perspective and for model correction, fixing outdated knowledge, and access revocation of the training dataset.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

Knowledge Hut

APRIL 23, 2024

Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data. Data warehousing offers several advantages. By structuring data in a predefined schema, data warehouses ensure data consistency and accuracy.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Knowledge Hut

APRIL 23, 2024

Big Data vs Small Data: Function Variety Big Data encompasses diverse data types, including structured, unstructured, and semi-structured data. It involves handling data from various sources such as text documents, images, videos, social media posts, and more.

Big Data 52

Big Data Datasets Data Analysis Media 52

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 91

Coding Building Management Government 91

Snowflake

NOVEMBER 11, 2024

However, scaling LLM data processing to millions of records can pose data transfer and orchestration challenges, easily addressed by the user-friendly SQL functions in Snowflake Cortex. Traditionally, SQL has been limited to structured data neatly organized in tables.

Unstructured Data 52

Unstructured Data SQL AWS Healthcare 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

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 Data Lake Management 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

Monte Carlo

MAY 28, 2024

Data Transformation : Clean, format, and convert extracted data to ensure consistency and usability for both batch and real-time processing. Data Loading : Load transformed data into the target system, such as a data warehouse or data lake. Used for identifying and cataloging data sources.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Knowledge Hut

JULY 24, 2023

NoSQL Databases NoSQL databases are non-relational databases (that do not store data in rows or columns) more effective than conventional relational databases (databases that store information in a tabular format) in handling unstructured and semi-structured data.

Database 52

Database NoSQL Management Relational Database 52

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 95

Data Lake Data Warehouse Cloud Unstructured Data 95

Knowledge Hut

MARCH 20, 2024

Data type defines the kind of value that can be stored in a variable or expression, acting as a classification system for data. Data structure, on the other hand, is a way to arrange and store data in computer memory so that it may be accessed and altered quickly. What is Data Type?

Computer Science 52

Computer Science Programming Language Algorithm Software Engineer 52

Snowflake

OCTOBER 28, 2024

Data gravity is the growing trend of application processes moving to the data rather than the other way around. It prioritizes the need to centralize data securely and reduces the need for costly movement across multiple systems. Amidst these dynamic forces, new trends have emerged.

Food 81

Food Media Retail Business Intelligence 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

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 86

Engineering Raw Data Data Science Machine Learning 86

Knowledge Hut

JANUARY 30, 2024

Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

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 Raw Data SQL 111

Striim

AUGUST 22, 2024

Whether you want to learn more about the benefits of real-time analytics or dive deeper into the most significant characteristics of a real-time analytics system, we’ll ensure you have a robust understanding of how real-time analytics move your business forward. Users can access this data on a dashboard, report, or another medium.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Knowledge Hut

JANUARY 29, 2024

Data wrangling offers several benefits, such as: Usable Data: Data wrangling converts raw data into a format suitable for analysis, ensuring the quality and integrity of the data used for downstream processes. Tabula : A versatile tool suitable for all data types, making it accessible for a wide range of users.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

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

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

Striim

OCTOBER 11, 2024

Change Data Capture (CDC) plays a key role here by capturing and streaming only the changes (inserts, updates, deletes) in real time, ensuring efficient data handling and up-to-date information across systems. Why are Data Pipelines Significant? Now that we’ve answered the question, ‘What is a data pipeline?’

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Ascend.io

FEBRUARY 28, 2024

And crucially, what does the future hold for data engineering in an AI-driven world? While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Knowledge Hut

MARCH 15, 2024

Making decisions in the database space requires deciding between RDBMS (Relational Database Management System) and NoSQL, each of which has unique features. RDBMS uses SQL to organize data into structured tables, whereas NoSQL is more flexible and can handle a wider range of data types because of its dynamic schemas.

NoSQL 52

NoSQL Database-centric Relational Database PostgreSQL 52

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

Knowledge Hut

JUNE 28, 2023

Focus Exploration and discovery of hidden patterns and trends in data. Reporting, querying, and analyzing structured data to generate actionable insights. Data Sources Diverse and vast data sources, including structured, unstructured, and semi-structured data.

Data Mining 52

Data Mining Business Intelligence BI Structured Data 52

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 Business Intelligence Machine Learning 97

Striim

MAY 1, 2024

UPS Capital recognizes the challenges faced by its customers in securing their package delivery ecosystem and is harnessing digital capabilities and data access to redefine traditional approaches, ensuring improved customer experiences and combating shipping loss.

Google Cloud 52

Google Cloud Insurance Finance Machine Learning 52

Precisely

SEPTEMBER 6, 2023

Similar laws in other jurisdictions are raising the stakes for enterprises, compelling them to govern their data more effectively than they have in the past. Traditional frameworks for data governance often work well for smaller volumes of data, and for highly structured data. Here are five to consider.

Data Governance 52

Data Governance Big Data Government Unstructured Data 52

Knowledge Hut

MARCH 18, 2024

Web services provide the protocols for communicating and exchanging data between different web applications irrespective of underlying platform or language. Imagine you have two separate systems: System A and System B. It is a message protocol specification for exchanging structured data.

Cloud Computing 52

Cloud Computing Programming Language Amazon Web Services Transportation 52

AltexSoft

JUNE 26, 2023

It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more. Data integration , on the other hand, happens later in the data management flow. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Monte Carlo

JULY 13, 2023

Data-driven organizations generate, collect, and store vast amounts of data. To effectively manage and analyze this data, data engineering teams must navigate a wide range of challenges, including data access, security, compliance, and data observability. Automating self-service access.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

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

Precisely

APRIL 30, 2024

Integration issues: Complex processes often involve interacting with multiple systems and applications. RPA might struggle to integrate seamlessly with all of them, especially legacy systems without modern APIs. These include: Structured data dependence: RPA solutions thrive on well-organized, predictable data.

Unstructured Data 59

Unstructured Data Government Data Validation Programming 59