How can you better manage large data sets?

Large Dataset, has become a new normal! They have huge strength to bring out pattern over the period. LD needs to be integrated, processed, stored and made easily available to Data Consumers, that's it! So easy, why do we need to discuss on it? As of now, we don't have unified strategy to manage Large Datasets. For storage, there are Relational MPP DB Systems, Non-Relational Hadoop Systems and Graph DB etc. Either, Use case or Organizational preference decides which one of them is good! So for a new normal, we are yet to get, unified solution. The solutions now are biased, opinionated, marketing driven. In my perspective, we should have common strategy to manage Large Datasets irrespective of tool/company.

One thing I found useful is to create 2 tables, one with data on the last period (last month, last quarter, etc.) and the second with the entire history. In 80% of requests for dashboards or analyses, the first table will be sufficient.

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Dependiendo del rango de número enteros se puede definir el tipo de dato de número mas peque?o que representa ese rango, para valores monetarios se puede definir un decimal de solo dos posiciones flotantes, tambien no definir cadenas de textos como varchar(MAX)

We must balance between normalized and denormalized datasets, guided by downstream usage patterns. When frequent joins occur between datasets, a denormalized form in a single table is often more practical, enhancing retrieval times and overall performance. However, this approach while optimizing read operations, can introduce data redundancy and increase storage needs. It's crucial to weigh these trade-offs, particularly in contexts where read performance is a priority, and to employ a nuanced approach to meet specific operational requirements effectively.

Data Modeling is a serious study in logic. I close the door and let no one in with the exception of a domain expert-user or detailed Project plan and an engineer that I can bounce ideas off of. Normalization can only go so far. Since this is a discussion about LD, indexing is tricky. It all depends on how fast you require the db to read the data. There are also methods besides tree indexing techniques like latent semantic indexing but that is too far afield. There a plenty of tools to assist you in conceptualizing your model but ultimately the data has to be presented in some form to the people who pay the salary. Erwin , pgadmin , are good tools a high level free tool is Plantuml.

Yes I concur with Yash, balancing normalized and demoralized helps, especially if it is more of a data product where configuration is needed. The most important for fast query time is to filter the data upfront to only have a list if INTEGER ID values that one can join against. A table variable or temp table would do for this. Good candidates would be Cuatomer ID, Candidate ID etc.

Además de quitar datos duplicados se podría filtrar el conjunto de datos dependiendo de los requisitos del proyecto de análisis de datos, en SQL Server es posible definir indices columnares para tablas de hechos en almacenes de datos

One thing I have found helpful is using partitions. With this determined proportion is filled per predefined time, as against full load of large datasets.

Definir bien las consultas SQL solo con las columnas necesarias, filtrando filas no necesarias para el análisis requerido, usando en la medida de lo posible Expresiones de Tabla (CTE).

Always spend enough time to ask key stakeholders on the scope of which sensitive/confidential data elements to bring into the final tables, don’t bring all PII data where it is not required. This saves the extra efforts down the road in reviewing and auditing access.

Data security ... painful but it needn't be. Keep the CIO happy by asking what should be encrypted. What laws and regs govern the data. Is it health information, government information? Make yourself familiar with the laws and rules of the data. Once you have that info, you can shop around for the best db's features and tools( RBAC, LDAP, Salting RSA, encryption.) You will have to collaborate with the head of security if you have one. It is imperative under new laws, restrictions and penalties that everyone knows about the security techniques employed.

We should recognize the significance of data quality, particularly when dealing with large datasets. It is paramount for the effective use of data analytics tools. To address the "garbage in, garbage out" challenge, it is crucial to prioritize meticulous data preprocessing, cleaning, and validation as actionable steps, ensuring that these critical processes are not overlooked and facilitating the generation of accurate insights.

PowerQuery solo es efectivo en conjuntos de datos relativamente chicos, en un conjunto de datos mediano(> 1 millon de registros) con varias agregaciones, aplicación de funciones lambda para definir columnas calculadas es mejor utilizar Python con la librería Pandas. Para conjuntos de datos que no caben en la memoria de una computadora o sea conjuntos de datos masivos (Big Data) se puede utilizar PySpark, Polars, Dask para su proceso.

Make sure that you have a snapshot of the prod database daily. If it's large provision some terabytes for a dump of the data and the schema. You will thank yourself for that. Also, keep a db for your dev team. Keep an air gap between the devices that access the db. Don't let any laptop with usb port or apps from the out side log into the database. Sleep well.

La optimización de la Base de Datos por parte del equipo de Administradores de Base de Datos, que las tablas tengan los indices necesarios para optimizar las consultas mas frecuentes, que las estadísticas de columnas se actualicen regularmente, que la información mas reciente este en medios de almacenamiento mas rápidos como discos de estado solido.