#Data Fabric

[neeraj82]

https://saxon.ai/blogs/turning-ai-aspirations-into-reality-with-microsoft-fabric-azure-ai/

[lisakeller22]

Data fabric and data lake are two different but compatible ways of processing and storing data. This blog explains the benefits and use cases of each approach, how they relate to each other, and how to choose the best data management approach for your business.

[biggest-gaudiest-patronuses]

the fact that shakespeare was a playwright is sometimes so funny to me. just the concept of the "greatest writer of the English language" being a random 450-year-old entertainer, a 16th cent pop cultural sensation (thanks in large part to puns & dirty jokes & verbiage & a long-running appeal to commoners). and his work was made to be watched not read, but in the classroom teachers just hand us his scripts and say "that's literature"

just...imagine it's 2450 A.D. and English Lit students are regularly going into 100k debt writing postdoc theses on The Simpsons screenplays. the original animation hasn't even been preserved, it's literally just scripts and the occasional SDH subtitles.txt. they've been republished more times than the Bible

[garymdm]

Precisely Enterworks: A Powerful Alternative to SAP MDM

Accurate and consistent master data is essential for businesses to succeed. Master data management (MDM) solutions help organizations create a single, trusted source of truth for their most critical data, such as customer, product, and supplier information. As organisations move away from monolithic, inflexible architectures, alternatives to SAP MDM are becoming more popular, even amongst SAP…

[oliviadlima]

Data Fabric Market Size, Growth Opportunities and Forecast

According to a recent report published by Allied Market Research, titled, “Data Fabric Market by Deployment, Type, Enterprise Size, and Industry Vertical: Global Opportunity Analysis and Industry Forecast, 2019–2026,” the data fabric market size was valued at $812.6 million in 2018, and is projected to reach $4,546.9 million by 2026, growing at a CAGR of 23.8% from 2019 to 2026.

Data fabric is a converged platform with an architecture and set of data services that provision diverse data management needs to deliver accurate IT service levels across unstructured data sources and infrastructure types. In the digital transformation era, data analytics has become a vital process that allows seamless flow of information and enables new customer touchpoints through technology. Therefore, data fabric has emerged as an innovative opportunity to enhance business agility.

Growth in cloud space have compelled services providers to rearchitect its storage platform. The rearchitected storage was opted to meet the demands of the services providers enterprise customers for high capacity, durability, performance, and availability, while still preserving their security posture of data storage and transfer. Data fabric is highly adopted as a rearchitect solution in form of infrastructure-as-a-service (IaaS) platform, owing to its benefits such as flexibility, scalability, replication, and others. This is a major factor that drives the growth of the global data fabric market during the forecast period.

Based on deployment, the cloud segment dominated the overall data fabric market in 2018, and is expected to continue this trend during the forecast period. This is attributed to rise in number of cloud deployment across the globe among various industry verticals as a scalable and on-demand data storage option. As data fabric can encompass a wide variety of data sources on disparate locations the deployment of data fabric solutions for cloud data is expected to rise significantly in the coming years among cloud service providers. This is expected to boost the data fabric market growth.

Banking, financial services, and insurance (BFSI) is a dominating sector in terms of technological adoption to gain highest competitive advantage. With rise in need to make smart decisions on the basis of heterogeneous data analysis which is gathered from a variety of sources, such as smartphones, IoT devices, social networks, rich media, and transaction systems, BFSI are embracing innovative solutions that deliver services at ease and speed. This has proliferated the demand for data fabric as it is capable to fulfill the needs of modern analytic, applications, and operational use cases that incorporates data from diverse sources such as files; tables; streams; logs; messaging; rich media, i.e., images, audio and video, and containers. Moreover, retail sector is expected to embrace the modern architecture functionality that offers scalable data analysis as the e-commerce activities are increasing the volume of data silos generated by these activities. This in turn creates lucrative opportunities for the players operating in the data fabric market trends.

Inquiry Before Buying: https://www.alliedmarketresearch.com/purchase-enquiry/6230

Key Findings of the Data Fabric Market :

By deployment, the cloud segment dominated the data fabric market. However, the On-premise segment is expected to exhibit significant growth during the forecast period in the data fabric industry.

Based on type, the disk-based data fabric segment accounted for the highest revenue dominated the data fabric market share in 2018.

Depending on enterprise size, the large enterprises generated the highest revenue in 2018. However, small and medium enterprises segment is expected to witness considerable growth in the near future.

Based on industry vertical, the BFSI segment generated the highest revenue in 2018. However, manufacturing is expected to witness considerable growth in the near future.

Region wise, Asia-Pacific is expected to witness significant growth in terms of CAGR in the upcoming years.

Some of the major players profiled in the data fabric market analysis include Denodo Technologies, Global IDs., Hewlett Packard Enterprise Company, IBM Corporation, NetApp, Oracle Corporation, SAP SE, Software AG, Splunk Inc., and Talend. Major players operating in this market have witnessed high growth in demand for cross-platform data management solutions especially due to growing disparate data sources in digital era.

About Us: Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Portland, Oregon. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of “Market Research Reports Insights” and “Business Intelligence Solutions.” AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.

[ibarrau]

[Fabric] Entre Archivos y Tablas de Lakehouse - SQL Notebooks

Ya conocemos un panorama de Fabric y por donde empezar. La Data Web nos mostró unos artículos sobre esto. Mientras más veo Fabric más sorprendido estoy sobre la capacidad SaaS y low code que generaron para todas sus estapas de proyecto.

Un ejemplo sobre la sencillez fue copiar datos con Data Factory. En este artículo veremos otro ejemplo para que fanáticos de SQL puedan trabajar en ingeniería de datos o modelado dimensional desde un notebook.

Arquitectura Medallón

Si nunca escuchaste hablar de ella te sugiero que pronto leas. La arquitectura es una metodología que describe una capas de datos que denotan la calidad de los datos almacenados en el lakehouse. Las capas son carpetas jerárquicas que nos permiten determinar un orden en el ciclo de vida del datos y su proceso de transformaciones.

Los términos bronce (sin procesar), plata (validado) y oro (enriquecido/agrupado) describen la calidad de los datos en cada una de estas capas.

Ésta metodología es una referencia o modo de trabajo que puede tener sus variaciones dependiendo del negocio. Por ejemplo, en un escenario sencillo de pocos datos, probablemente no usaríamos gold, sino que luego de dejar validados los datos en silver podríamos construir el modelado dimensional directamente en el paso a "Tablas" de Lakehouse de Fabric.

NOTAS: Recordemos que "Tablas" en Lakehouse es un spark catalog también conocido como Metastore que esta directamente vinculado con SQL Endpoint y un PowerBi Dataset que viene por defecto.

¿Qué son los notebooks de Fabric?

Microsoft los define como: "un elemento de código principal para desarrollar trabajos de Apache Spark y experimentos de aprendizaje automático, es una superficie interactiva basada en web que usan los científicos de datos e ingenieros de datos para escribir un código que se beneficie de visualizaciones enriquecidas y texto en Markdown."

Dicho de manera más sencilla, es un espacio que nos permite ejecutar bloques de código spark que puede ser automatizado. Hoy por hoy es una de las formas más populares para hacer transformaciones y limpieza de datos.

Luego de crear un notebook (dentro de servicio data engineering o data science) podemos abrir en el panel izquierdo un Lakehouse para tener referencia de la estructura en la cual estamos trabajando y el tipo de Spark deseado.

Spark

Spark se ha convertido en el indiscutible lenguaje de lectura de datos en un lake. Así como SQL lo fue por años sobre un motor de base de datos, ahora Spark lo es para Lakehouse. Lo bueno de spark es que permite usar más de un lenguaje según nuestro comodidad.

Creo que es inegable que python está ocupando un lugar privilegiado junto a SQL que ha ganado suficiente popularidad como para encontrarse con ingenieros de datos que no conocen SQL pero son increíbles desarrolladores en python. En este artículo quiero enfocarlo en SQL puesto que lo más frecuente de uso es Python y podríamos charlar de SQL para aportar a perfiles más antiguos como DBAs o Data Analysts que trabajaron con herramientas de diseño y Bases de Datos.

Lectura de archivos de Lakehouse con SQL

Lo primero que debemos saber es que para trabajar en comodidad con notebooks, creamos tablas temporales que nacen de un esquema especificado al momento de leer la información. Para el ejemplo veremos dos escenarios, una tabla Customers con un archivo parquet y una tabla Orders que fue particionada por año en distintos archivos parquet según el año.

CREATE OR REPLACE TEMPORARY VIEW Dim_Customers_Temp USING PARQUET OPTIONS ( path "Files/Silver/Customers/*.parquet", header "true", mode "FAILFAST" ) ;

CREATE OR REPLACE TEMPORARY VIEW Orders USING PARQUET OPTIONS ( path "Files/Silver/Orders/Year=*", header "true", mode "FAILFAST" ) ;

Vean como delimitamos la tabla temporal, especificando el formato parquet y una dirección super sencilla de Files. El "*" nos ayuda a leer todos los archivos de una carpeta o inclusive parte del nombre de las carpetas que componen los archivos. Para el caso de orders tengo carpetas "Year=1998" que podemos leerlas juntas reemplazando el año por asterisco. Finalmente, especificamos que tenga cabeceras y falle rápido en caso de un problema.

Consultas y transformaciones

Una vez creada la tabla temporal, podremos ejecutar en la celda de un notebook una consulta como si estuvieramos en un motor de nuestra comodidad como DBeaver.

Escritura de tablas temporales a las Tablas de Lakehouse

Realizadas las transformaciones, joins y lo que fuera necesario para construir nuestro modelado dimensional, hechos y dimensiones, pasaremos a almacenarlo en "Tablas".

Las transformaciones pueden irse guardando en otras tablas temporales o podemos almacenar el resultado de la consulta directamente sobre Tablas. Por ejemplo, queremos crear una tabla de hechos Orders a partir de Orders y Order details:

CREATE TABLE Fact_Orders USING delta AS SELECT od.*, o.CustomerID, o.EmployeeID, o.OrderDate, o.Freight, o.ShipName FROM OrdersDetails od LEFT JOIN Orders o ON od.OrderID = o.OrderID

Al realizar el Create Table estamos oficialmente almacenando sobre el Spark Catalog. Fíjense el tipo de almacenamiento porque es muy importante que este en DELTA para mejor funcionamiento puesto que es nativo para Fabric.

Resultado

Si nuestro proceso fue correcto, veremos la tabla en la carpeta Tables con una flechita hacia arriba sobre la tabla. Esto significa que la tabla es Delta y todo está en orden. Si hubieramos tenido una complicación, se crearía una carpeta "Undefinied" en Tables la cual impide la lectura de nuevas tablas y transformaciones por SQL Endpoint y Dataset. Mucho cuidado y siempre revisar que todo quede en orden:

Pensamientos

Así llegamos al final del recorrido donde podemos apreciar lo sencillo que es leer, transformar y almacenar nuestros modelos dimensionales con SQL usando Notebooks en Fabric. Cabe aclarar que es un simple ejemplo sin actualizaciones incrementales pero si con lectura de particiones de tiempo ya creadas por un data engineering en capa Silver.

¿Qué hay de Databricks?

Podemos usar libremente databricks para todo lo que sean notebooks y procesamiento tal cual lo venimos usando. Lo que no tendríamos al trabajar de ésta manera sería la sencillez para leer y escribir tablas sin tener que especificar todo el ABFS y la característica de Data Wrangler. Dependerá del poder de procesamiento que necesitamos para ejecutar el notebooks si nos alcanza con el de Fabric o necesitamos algo particular de mayor potencia. Para más información pueden leer esto: https://learn.microsoft.com/en-us/fabric/onelake/onelake-azure-databricks

Espero que esto los ayude a introducirse en la construcción de modelados dimensionales con clásico SQL en un Lakehouse como alternativa al tradicional Warehouse usando Fabric. Pueden encontrar el notebook completo en mi github que incluye correr una celda en otro lenguaje y construcción de tabla fecha en notebook.

[lumendata]

enterprise data fabric

How Data-Fabric can Maximize the Value of Business Data and Accelerate Digital Transformation

Data has the potential to help businesses quickly adapt to change, improve access and visibility of relevant data to stakeholders and stay agile. Navigating data gathered with the exponential growth of businesses becomes a challenge and this can be handled with the help of Data fabric. In this blog, we will help you understand the following about Data Fabric for Business-

Understand Data Fabric and its components.

Growth with Data Fabric, the scope of gaining key insights for decision making.

Value of Data Fabric in digital transformation.

Driving business innovation and growth. 

Data fabric combines both human and machine functionalities to help businesses access data in place or support its consolidation where required. It continuously identifies and integrates data from disparate applications to discover insightful, business-relevant relationships between available data points. Further, it also handles the repair of failed data integration jobs and auto-profiling of datasets. 

Components of data fabric- 

Data Processing helps provide clear analytics-ready data by curating and transforming data for Business Intelligence and Artificial Intelligence.

Data Orchestration coordinates data flow and helps the business with a comprehensive view of the data pipeline.

Data Ingestion works with data spread across various sources such as databases, cloud source applications, and data streams.

Data governance centralizes the entire data governance process of the business and helps it manage metadata locally and in compliance with corporate policies. 

Click on enterprise data fabric to know more.

[neeraj82]

https://saxon.ai/services/microsoft-fabric-consulting-services/

[lisakeller22]

Evaluating Data Fabric and Data Lake Architectures

Choosing a data management approach can be challenging, especially when you have to compare data lake and data fabric. Go through the blog to learn to understand the key differences between them and make an informed decision.

[themachinethatdoeseverything]

GUYS IN JAIL CELLS

[bradleycarlgeiger]

IT'S RETARDEDLY STUPID THAT YOU DON'T SEEM TO BE ABLE TO IMAGINE EFFECTS BEING PRODUCED TO SUPPORT FABRICATED DATA.

[bmpmp3]

some stuff i did in school recently: scanned images of fabric that have been run through audacity to be glitched up with reverb and echo and such and then printed back out and embroidered! i thought it was kind of funny

here's the original fabric:

its old busted pajamas <3

[memento-mariii]

As much as I enjoy unethical (pseudo)science in fiction, in real life killing them with my mind is not enough I need to strangle these people with my own two hands.

Your methodology is bad and you should feel bad.