Knowledge Hut

APRIL 23, 2024

Data storing and processing is nothing new; organizations have been doing it for a few decades to reap valuable insights. Compared to that, Big Data is a much more recently derived term. So, what exactly is the difference between Traditional Data and Big Data? Data Size Much larger in size, volume, and variety.

Big Data 52

Big Data Relational Database Data Datasets 52

ThoughtSpot

MARCH 5, 2024

While AI-powered, self-service BI platforms like ThoughtSpot can fully operationalize insights at scale by delivering visual data exploration and discovery, it still requires robust underlying data management. Snowflake's new dynamic tables feature redefines how BI and analytics teams approach data transformation pipelines.

BI 94

BI Datasets Raw Data SQL 94

Hevo

AUGUST 15, 2023

MongoDB Atlas excels at storing and processing unstructured and semi-structured data, while PostgreSQL offers scalability and advanced analytics. MongoDB Atlas to PostgreSQL integration forms a robust ecosystem that addresses the technical challenges associated with data management and analysis.

MongoDB 52

MongoDB PostgreSQL Structured Data Data 52

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 Data Cleanse 52

Data Engineering Podcast

AUGUST 31, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Data Warehouse 100

Data Warehouse Cloud Building Data Lake 100

Data Engineering Podcast

DECEMBER 11, 2022

Summary Data is one of the core ingredients for machine learning, but the format in which it is understandable to humans is not a useful representation for models. Embedding vectors are a way to structure data in a way that is native to how models interpret and manipulate information. images, audio, video, etc.)

Unstructured Data 100

Unstructured Data Machine Learning Data Engineering Data Engineer 100

Striim

MAY 1, 2024

The sheer volume of data generated from the increasing package deliveries overwhelmed existing data management systems, underscoring a critical need for more advanced data handling capabilities. The absence of real-time data processing capabilities hindered UPS Capital’s risk management and rapid response efforts.

Google Cloud 52

Google Cloud Insurance Finance Machine Learning 52

Data Engineering Podcast

DECEMBER 8, 2019

If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.

Data Warehouse 100

Data Warehouse Cloud AWS Relational Database 100

Databand.ai

JULY 17, 2023

DataOps , short for Data Operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data management processes. It aims to streamline the entire data lifecycle—from ingestion and preparation to analytics and reporting.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Data Engineering Podcast

JULY 1, 2018

Cheryl Martin, Chief Data Scientist for Alegion, discusses the importance of properly labeled information for machine learning and artificial intelligence projects, the systems that they have built to scale the process of incorporating human intelligence in the data preparation process, and the challenges inherent to such an endeavor.

Metadata 100

Metadata Machine Learning Data Preparation Data Collection 100

Data Engineering Podcast

FEBRUARY 9, 2020

If you’re struggling with unwieldy dimensional models, slow moving projects, or challenges integrating new data sources then listen in on this conversation and then give data vault a try for yourself. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council.

Data Lake 100

Data Lake Data Warehouse Hadoop NoSQL 100

AltexSoft

AUGUST 29, 2023

If your organization fits into one of these categories and you’re considering implementing advanced data management and analytics solutions, keep reading to learn how data lakes work and how they can benefit your business. Data sources can be broadly classified into three categories. Structured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 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 109

IT Unstructured Data Data Architecture Government 109

Knowledge Hut

NOVEMBER 3, 2023

The need for efficient and agile data management products is higher than ever before, given the ongoing landscape of data science changes. MongoDB is a NoSQL database that’s been making rounds in the data science community. There are several benefits to MongoDB for data science operations.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

DataKitchen

FEBRUARY 27, 2024

A well-structured Data Journey ensures the quality and reliability of the data feeding into LLMs, laying the groundwork for trust and efficacy in AI-driven processes. As data engineers and technology leaders navigate this landscape, staying informed and proactive in data management practices is paramount.

Data Engineering 48

Data Engineering Data Engineer Engineering Unstructured Data 48

phData: Data Engineering

SEPTEMBER 19, 2023

With many data modeling methodologies and processes available, choosing the right approach can be daunting. This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Ascend.io

FEBRUARY 28, 2024

Read More: AI Data Platform: Key Requirements for Fueling AI Initiatives How Data Engineering Enables AI Data engineering is the backbone of AI’s potential to transform industries , offering the essential infrastructure that powers AI algorithms.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Data Engineering Podcast

FEBRUARY 3, 2019

In this episode Eric Kansa describes how they process, clean, and normalize the data that they host, the challenges that they face with scaling ETL processes which require domain specific knowledge, and how the information contained in connections that they expose is being used for interesting projects.

Digital Media 100

Digital Media Media PostgreSQL Datasets 100

Knowledge Hut

MARCH 27, 2024

Database vs Data Structure: Purpose Database: Designed for efficient storage, retrieval, and management of extensive data sets. Supports complex query relationships and ensures data integrity. Commonly used in business and web development for structured data storage. How Are They Similar?

Database 52

Database Algorithm Relational Database PostgreSQL 52

Cloudera

DECEMBER 11, 2020

Powered and supported by Cloudera, this framework brings together disparate data sources, combining internal data with public data, and structured data with unstructured data. It can also prevent unauthorized data access, decrease operational costs, and greatly increase business agility for multiple users.

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

Edureka

AUGUST 2, 2023

It is a crucial tool for data scientists since it enables users to create, retrieve, edit, and delete data from databases.SQL (Structured Query Language) is indispensable when it comes to handling structured data stored in relational databases. Data scientists use SQL to query, update, and manipulate data.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

Knowledge Hut

DECEMBER 26, 2023

The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications. Data Engineers Data engineers are IT professionals whose responsibility is the preparation of data for operational or analytical use cases.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

Knowledge Hut

DECEMBER 26, 2023

Data Engineering Data Engineering involves various operations like creating data pipelines, strategizing data management plans, processing data, and many more. Data Management Data management is an integral part of high-scale organizations.

Data Science 52

Data Science Scala Programming Language R (Programming) 52

Monte Carlo

JULY 13, 2023

In this article, we will explore how automation can help data engineering teams overcome these challenges and provide a more seamless and effective data management process. Automated processes scan all data stores and discover data, including semi-structured data such as JSON on a continual basis.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Monte Carlo

APRIL 24, 2023

If not paired with Glue, or another metastore/catalog solution, S3 will also lack some of the metadata structure required for more advanced data management tasks. AWS is one of the most popular data lake vendors. The added structure and governance from Dataplex makes BigLake an intriguing data lakehouse option as well.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 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

ProjectPro

FEBRUARY 16, 2023

Focus Historical data analysis, reporting, and visualization. Input Data Structured data from various sources, such as databases, spreadsheets, and ERP systems. Structured, semi-structured, and unstructured data from multiple sources, such as social media, IoT devices, and sensors.

Business Intelligence 52

Business Intelligence BI Data Mining Raw Data 52

Knowledge Hut

DECEMBER 29, 2023

Specific Skills and Knowledge: Data collection and storage optimization Data processing and interpretation Reporting and displaying statistical and pattern information Developing and evaluating models to handle huge amounts of data Understanding programming languages C. It separates the hidden links and patterns in the data.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Knowledge Hut

DECEMBER 6, 2023

Because everything in today's world is data-driven, employees with skill and understanding in data management are highly valued and rewarded with the greatest pay packages. The educational requirement for the field of Data Science is preferably a B.E/B.Tech

Programming Language 52

Programming Language Data Cleanse Computer Science Education 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 Data Lake Management Data Governance 52

Silectis

JANUARY 3, 2021

Data Architecture Data architecture is a composition of models, rules, and standards for all data systems and interactions between them. Data Catalog An organized inventory of data assets relying on metadata to help with data management. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

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

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.

Data Governance 79

Data Governance Government Generalist Data Engineering 79

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

ProjectPro

SEPTEMBER 9, 2021

Spark SQL, for instance, enables structured data processing with SQL. Apache Hive and Apache Spark are two popular big data tools for data management and Big Data analytics. The tool offers a rich interface with easy usage by offering APIs in numerous languages, such as Python, R, etc.

Hadoop 52

Hadoop Big Data Tools Java SQL 52