How can you use data lake architecture to democratize data?

Data lake architecture refers to a centralized repository that allows for the storage of structured and unstructured data at any scale. It enables diverse data sources, like logs, XML, and relational data, to be stored in their native format. This architecture democratizes data by making it accessible and analyzable for varied business needs.

La arquitectura de lago nace gracias a la flexibilidad de la nube. Hasta ahora se utilizaban arquitecturas de Big Data para procesar grandes volúmenes de datos y era necesario disponer de muchos servidores de computo y el conocimiento necesario para administrarlo. La ventaja de la arqutiectura de lago consiste en separar la capa de almacenamiento de la capa de computo. De esta manera se pueden usar solo los recursos necesarios dependiendo del volumen de datos. La segunda ventaja consiste en que las diferentes herramientas de computo pueden acceder a los datos, tales como: herramientas de BI, bases de datos, soluciones de machine learning o integración.

Data lake architecture is used to democratize data because it supports diverse data types and large volumes, making it accessible for different user needs. In industries like finance, it can combine transactional data with market trends, providing a holistic view for decision-making, thereby empowering various stakeholders to access and analyze relevant data efficiently.

With the rise of cloud computing, data lakes became ubiquitous, but it was soon realized that having various data formats wasn't optimal for querying. In my project, I prioritize converting data into Delta Tables to achieve better performance, transactional support, and seamless data discovery using a catalog solution like Databricks Unity Catalog or other open-source alternatives. However, not everything needs to be migrated; I carefully balance my projects based on operational costs. Nonetheless, all data must be cataloged for easy access by anyone who needs it. A catalog is key to preventing your data lake from turning into a swamp.

Mientras las soluciones de Big Data eran aprovechadas principalmente por grandes comp?ías, la flexibilidad de esta arquitectura permite que cualquier empresa pueda aprovechar sus datos. Los usuarios como los Data Engineer pueden usar servicios gestionados para obtener información útil con el mínimo esfuerzo en la gestión de las plataformas. Además permite analizar datos estructurados y no estructurados, aportando gran valor a las empresas que lo usan y que no podian usar dichos datos anteriormente.

Data lake architecture centralizes diverse data types, allowing storage of structured and unstructured data at scale. It promotes flexibility for analytics, encourages collaboration among different data teams, and accommodates big data processing. This architecture enables businesses to extract valuable insights efficiently from vast and varied datasets.

Muchas arquitecturas se centran en el movimiento de datos para ponerlos en las distintas plataformas que tienen distintos usos. Esto hace que los analistas y los usuarios finales se centren mas en el ETL que en aprovechar los datos. Esta falta de flexibilidad hace que sea dificil democratizar los datos. Es altamente recomendable utilizar una plataforma que permita utilizar cualquier tipo de dato y que permita consultarlo de cualquier forma. Por ejemplo, la base de datos Autonomous de Oracle permite crear una arquitectura de lago mientras que los datos se pueden consultar por cualquier via como: REST, SQL, JDBC, GraphQL, Delta Sharing o con database links que consultas bases de datos de 3o sin movimiento de datos.

Implement a data catalog solution promptly. If you're using Databricks, I recommend Unity Catalog. Otherwise, consider excellent open-source solutions like "Open Metadata" and "DataHub Project" for comprehensive data lineage across Delta Lake environments. Additionally, there are paid solutions such as Azure PurView, Informatica, AWS Glue, etc. However, be mindful that data ingestion processes can incur significant costs if not properly planned, and pricing for these solutions may be complex to estimate and understand.

Muchos clientes no podrían utilizar los datos historicos, por falta de almacenamiento o por el coste que eso conlleva. Esta arquitectura permite no solo ahorrar costes, sino además tener los pilares fundamentales para poder hacer Machine Learning o IA para mejorar la productividad de la empresa.

In the realm of data accessibility, a visionary architect painted a narrative of empowerment with data lake architecture. * The data lake, a vast reservoir, became the democratizing force, pooling diverse data sources into a unified expanse. * Self-service analytics tools emerged as protagonists, granting every user the power to navigate and extract insights. * Metadata catalogs played the role of storytellers, providing a navigational guide to the expansive lake. This narrative where data lake architecture, self-service analytics, and metadata catalogs converged, creating a landscape where data democratization wasn't just a vision but a reality, empowering users to navigate the vast waters of information with ease.