Knowledge Hut

OCTOBER 30, 2023

Data Transformation and ETL: Handle more complex data transformation and ETL (Extract, Transform, Load) processes, including handling data from multiple sources and dealing with complex data structures. Ensure compliance with data protection regulations. Define data architecture standards and best practices.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Monte Carlo

AUGUST 31, 2023

We’ll take a closer look at variables that can impact your data next. Migration to the cloud Twenty years ago, your data warehouse (a place to transform and store structured data) probably would have lived in an office basement, not on AWS or Azure. What is a decentralized data architecture?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Knowledge Hut

NOVEMBER 28, 2023

The data goes through various stages, such as cleansing, processing, warehousing, and some other processes, before the data scientists start analyzing the data they have garnered. The data analysis stage is important as the data scientists extract value and knowledge from the processed, structured data.

Data Science 52

Data Science Telecommunication Recruitment Finance 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

AltexSoft

AUGUST 29, 2023

Also, data lakes support ELT (Extract, Load, Transform) processes, in which transformation can happen after the data is loaded in a centralized store. A data lakehouse may be an option if you want the best of both worlds. Data sources can be broadly classified into three categories. Structured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Silectis

JANUARY 3, 2021

Big Data Processing In order to extract value or insights out of big data, one must first process it using big data processing software or frameworks, such as Hadoop. Big Query Google’s cloud data warehouse. Data Catalog An organized inventory of data assets relying on metadata to help with data management.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Rockset

AUGUST 2, 2023

The vessel positions data which in nature is a time series geospatial data set, was stored in both PostgreSQL and Cassandra to be able to support different use cases. Furthermore, as Windward introduced new use cases they started to hit limitations with their data stack.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52

ProjectPro

JUNE 6, 2015

At ProjectPro we had the pleasure to invite Abed Ajraou , the Director of the BI & Big Data in Solocal Group (Yellow Pages in France) to speak about the digital transformation from BI to Big Data. The goal of BI is to create intelligence through Data. The goal of BI is to create intelligence through Data.

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

ProjectPro

FEBRUARY 16, 2023

The SQL-on-Hadoop platform combines the Hadoop data architecture with traditional SQL-style structured data querying to create a specific analytical application tool. Data engineers can extract data from the Hadoop system using Hive and Impala , which offer an SQL-like interface.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

AltexSoft

OCTOBER 25, 2021

But a lot of data (by different estimations, 70 or 80 percent of all clinical data) remains unstructured , kept in textual reports, clinical notes, observations, and other narrative text. Unstructured data is unavoidable, yet extremely valuable. However useful, CDSSs are mostly limited to processing only structured data.

Medical 52

Medical Healthcare Process Hospitality 52

Rockset

APRIL 26, 2023

We’ve noticed many common patterns across streaming data architectures and we’ll be sharing a blueprint for three of the most popular: anomaly detection, IoT, and recommendations. In this post, we’ll get to the details: how does one design an RTA system? We’ve been helping customers implement real-time analytics since 2018.

Architecture 52

Architecture Transportation Data Lake Insurance 52

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Central to this transformation are two shifts.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Rockset

JANUARY 28, 2020

Testing new functionality on their transactional data store is costly and can impact production. Ad hoc queries to measure the accuracy of the checkout process in real time are not possible with traditional data architectures. Standard productionizes several endpoints a day using this methodology.

Retail 40

Retail Google Cloud Raw Data Data Lake 40

Knowledge Hut

MAY 4, 2023

A Data Engineer is a professional who deals with data-related tasks such as creating, testing, and maintaining an organization's data infrastructure. Data engineers are professionals who play a consistent role in building data warehouses to store data and data pipelines to feed data into those structures.

Data Engineering 52

Data Engineering Data Engineer Engineering Banking 52

Cloudera

MAY 14, 2024

To attain that level of data quality, a majority of business and IT leaders have opted to take a hybrid approach to data management, moving data between cloud, on-premises -or a combination of the two – to where they can best use it for analytics or feeding AI models. What do we mean by ‘true’ hybrid? Let’s dive deeper.

Cloud 83

Cloud Data Governance Unstructured Data Data Architecture 83

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

JULY 22, 2016

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream data –Data Lakes fulfil a critical need. Data Warehouses do not retain all data whereas Data Lakes do.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineer Salary by Skills The roles and responsibilities of a Big Data Engineer in an organization vary as per the business domain, type of the project, specific big data tools in use, IT infrastructure, technology stack, and a lot more.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

SEPTEMBER 26, 2023

It leverages a Massively Parallel Processing (MPP) architecture, which is optimized for executing complex analytical queries on large datasets efficiently. This makes it an excellent choice for organizations that need to analyze large volumes of structured and semi-structured data quickly and effectively.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Towards Data Science

FEBRUARY 15, 2023

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture. The proposal is simple — “Trow everything you have here inside and worry later”.

Data Lake 80

Data Lake Data Warehouse Hadoop Data Architecture 80

Snowflake

NOVEMBER 2, 2023

And, since historically tools and commercial platforms were often designed to align with one specific architecture pattern, organizations struggled to adapt to changing business needs – which of course has implications on data architecture. The schema of semi-structured data tends to evolve over time.

Data Lake 91

Data Lake Data Warehouse Cloud Unstructured Data 91

Cloudera

SEPTEMBER 12, 2022

First, organizations have a tough time getting their arms around their data. More data is generated in ever wider varieties and in ever more locations. 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. Unified data fabric.

Unstructured Data 74

Unstructured Data Data Lake Data Architecture Data 74

ProjectPro

NOVEMBER 14, 2015

This data can be analysed using big data analytics to maximise revenue and profits. We need to analyze this data and answer a few queries such as which movies were popular etc. To this group, we add a storage account and move the raw data. Then we create and run an Azure data factory (ADF) pipelines.

Hadoop 52

Hadoop Project Big Data Healthcare 52

Ascend.io

JULY 17, 2023

If you work with data, you’ll have come across the term data mesh by now. This decentralized but interconnected approach to structuring data has become increasingly popular since the term was coined by Zhamak Dehghani 4 years ago. Essentially, you’re risking scaling up your problems along with your data architecture.

Datasets 52

Datasets Telecommunication Architecture Government 52

Monte Carlo

APRIL 1, 2021

Unstructured data is problematic as it relates to data catalogs because it’s not organized, and if it is, it’s often not declared as organized. While modern data architectures, including data lakes, are often distributed, data catalogs are usually not, treating data like a one-dimensional entity.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 52

U-Next

AUGUST 26, 2022

Binarized Search Trees: Simple data architectures are grid search trees. They enable numerous solutions for a certain issue, letting the user choose the optimal data structure to address the issue. The Benefits Of Data Structures. Utilizing a data structure makes retrieving data from a storage source easier.

Algorithm 40

Algorithm Data Utilities Data Science 40

Snowflake

MARCH 20, 2023

Hub Leipzig began by replicating its sorting machine data in the Snowflake platform, expanding its existing structured data sources from three to 50. We generate data all the time even when the machinery is not running,” said Wittfoth.

Cloud 66

Cloud Business Intelligence Data Science Data 66

AltexSoft

MARCH 2, 2023

Data modeling involves creating a conceptual representation of data objects and their relationships to each other, as well as the rules governing those relationships. To design an effective data governance program, it’s crucial to choose an operational model that fits your business size and structure.

Data Governance 52

Data Governance Government Healthcare Programming 52

Rockset

FEBRUARY 24, 2023

Introduction Let’s get this out of the way at the beginning: understanding effective streaming data architectures is hard, and understanding how to make use of streaming data for analytics is really hard. Kafka or Kinesis ? Stream processing or an OLAP database? Open source or fully managed?

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Monte Carlo

JUNE 14, 2023

Does data quality need to be high will directionally accurate suffice? Let the data drive the data pipeline architecture. Most data teams will be handling mostly structured data for analytical purposes making a data warehouse based data pipeline architecture a natural fit.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

JULY 18, 2023

Spark SQL brings native support for SQL to Spark and streamlines the process of querying semistructured and structured data. It incorporates a comprehensive set of libraries, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

Big Data 52

Big Data Data Process Process Hadoop 52

ProjectPro

JANUARY 19, 2022

Data engineering is a new and ever-evolving field that can withstand the test of time and computing developments. Companies frequently hire certified Azure Data Engineers to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

AltexSoft

JULY 19, 2022

What data mesh is and is not. What data mesh IS. Data mesh is a set of principles for designing a modern distributed data architecture that focuses on business domains, not the technology used, and treats data as a product. For example, your organization has an HR platform that produces employee data.

Architecture 52

Architecture Data Lake Medical Datasets 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Traditional data warehouse platform architecture. Unstructured and streaming data support.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Knowledge Hut

DECEMBER 26, 2023

The job of a data engineer is to develop models using machine learning to scan, label and organize this unstructured data. This process helps convert the unstructured data into structured data, which can easily be collected and interpreted using analytical tools.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

ProjectPro

JANUARY 31, 2022

Snowflake in Action at Western Union Snowflake's multi-cluster shared data architecture expanded instantaneously to serve Western Union's data, users, and workloads without causing resource conflict. Snowflake saves and manages data on the cloud using a shared-disk approach, making data management simple.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

JANUARY 31, 2023

Data Variety Hadoop stores structured, semi-structured and unstructured data. RDBMS stores structured data. Data storage Hadoop stores large data sets. RDBMS stores the average amount of data. Works with only structured data. Hardware Hadoop uses commodity hardware.

Big Data 52

Big Data Hadoop AWS Relational Database 52