What role does data analytics play in choosing between a data lake and a warehouse?

Why not choose a platform, that performs equivalent to a data warehouse and is flexible like a data lake? Better to go with data lakehouse kind of capability, which is offered by a variety of data platform vendors. I may recommend any of the following at this moment 1. #Vertica: Excellent performance, horizontally scalable, flexible deployments on any cloud/on-prem, separate compute and storage, and optimal costing. 2. #Snowflake: Excellent performance, less support required, storage cost is very low but compute is high. Provide good concurrency. 3 #Databricks: Great SQL and performance capabilities. Indexing, caching, and MPP processing to make BI work rapidly on data lakes.

Data storage dilemma? Imagine decluttering! Pick a wardrobe (data warehouse) for your favorite clothes (structured data, reports) - organized for quick outfit choices (analysis, decisions). Limited space though (storage). An attic (data lake) stores everything (all data types) - toss anything in (flexible storage) but finding things might take time. A data analyst, like your decluttering helper, picks the right option: Wardrobe for clear needs, attic for exploring! Sometimes, you can have both! Imagine having both a wardrobe (data warehouse) and an attic (data lake) working together. The analyst can grab relevant items from the attic (unstructured) and organize them in the wardrobe (structured) for easier access.

If any organizations want to choose between Data Lake or Data Warehouse then Data Lake will be the ideal choice for those organizations. There are some of the reasons which organizations can look if they are choosing between these two, 1. Data warehouse are expensive as compare to Data Lake because they are only storing the structured data 2. Data Lake offers more storage options for storing the data. 3. As per organizations they hold every possible ways of data related to relational or non relational data so keeping that point Data Lake will be the best option as it deals with every format i.e raw, semi-structured, structured data.

Data analytics plays a crucial role in choosing between a data lake and a data warehouse by determining the nature of the data to be processed (structured vs. unstructured) and the type of analysis required (real-time vs. batch processing), guiding the selection based on the specific needs for storage, processing, and analysis.

Data analytics helps to decide whether to use a data lake or a data warehouse by analyzing data needs and usage patterns. A data lake is good for storing large amounts of raw data from various sources, which I can then analyze in different ways. This is useful if I need flexibility and want to run complex analyses. On the other hand, a data warehouse is better for storing structured data that I need to access quickly for regular reporting and business intelligence. By understanding how I plan to use my data—whether I need raw, flexible data or structured, fast-access data—data analytics guides me in choosing the right option for my needs.

For performing the most efficient-business intelligence tasks and creating a analytical dashboard-we need to have a structured data and it's proper view of understanding .

The choice between a data lake and a data warehouse hinges on the specific data analytics requirements of the organization. Organizations should opt for a data warehouse if the primary need is for structured data, high-performance complex queries, and traditional BI tasks with stringent data quality and governance requirements. They should however choose a data lake if there is a need to handle diverse data types, large volumes, real-time data processing, and advanced analytics like machine learning and data science, while also considering cost-effectiveness for storage.

O querido dos self-service BIs , onde o jogo come?a ou termina , dados conhecidos podem ser vistos ou alimentados com o que pode sair de resultados de ML ou mesmo bricks de dados que vieram do data lake... expandindo as vis?es atuais já consolidadas ou mesmo , rápido e pratico para KPIs naquelas reuni?es de resultado

Data warehouses are structured specifically to support efficient querying and reporting. If your main goal is business intelligence tasks like generating reports and dashboards, then using data analytics to assess your needs can be crucial. It helps determine if a traditional data warehouse suits you better, especially if you require high data quality and consistency for quick access to reliable, structured information.

Definition: Data warehouses are structured to store processed, refined data and are typically used for specific purposes and queries. Role of Analytics: Data analytics can assess if the structured query capabilities of data warehouses align better with the organization’s needs for standard reporting and business intelligence.

Data volume always is one of the most effective parameter to choose the initial process as like for big organizations and huge dataset with more storage requirement-data-lake is one of the effective procedure .

The amount of data you're dealing with can really sway your decision. Using data analytics to forecast data growth and storage needs helps figure this out. For huge datasets that are going to keep growing, a data lake might be the way to go—it's more scalable and could be more cost-effective. But if your data isn't overwhelming and it's mostly structured, sticking with a data warehouse could be all you need.

Data analytics plays a crucial role in choosing between a data lake and a data warehouse by assessing the volume and variety of data that needs to be stored and processed. Data lakes are better suited for handling large volumes of raw, unstructured data from diverse sources, making them ideal for big data analytics and machine learning applications. In contrast, data warehouses are optimized for structured data and are used for traditional business intelligence and reporting. The choice depends on the specific data requirements and analytical goals of the organization.

Consideration: The volume of data an organization generates is critical. Data analytics helps understand the scale and growth rate of data. Application: If data volumes are massive and expected to grow rapidly, a data lake might be more suitable due to its scalability.

Acredito que neste cenário o analista de dados precisa atuar com o engenheiro , deixar a decis?o totalmente na m?o do analista pode ter consequências no futuro, no melhor dos cenários, a famosa reuni?o de avali??o de tecnologias ajuda a ter um futuro mais assertivo

When choosing between a data lake and a data warehouse for healthcare analytics, processing needs are crucial. - Data Lake: Ideal for handling large volumes of raw, unstructured, and semi-structured data. It offers flexibility and cost-effective storage, making it suitable for advanced analytics and machine learning. - Data Warehouse: Best for structured data requiring high-quality processing, fast analytics, and robust data governance. It excels in supporting complex queries and business intelligence. Balancing these options ensures an optimal data strategy tailored to healthcare analytics needs.

Your processing requirements are another critical factor to consider. Data analytics can shed light on the necessary processing capabilities for your data. If you need to handle complex processing tasks, like using big data frameworks such as Apache Hadoop, or if you're into advanced analytics like real-time processing or predictive modeling, a data lake is likely your best bet. On the other hand, if your needs are more about straightforward, routine batch processing, then a data warehouse might just do the trick.

Analysis: Evaluate the processing needs through data analytics to understand whether the data requires real-time processing or batch processing. Decision Influence: Data lakes generally provide more flexibility for real-time processing using tools like Apache Hadoop or Spark, while data warehouses offer efficient batch processing for structured data.

Accessibility: Analytics can identify who needs access to the data and for what purposes. Implication: If broad access is needed for diverse analytical needs and exploratory data science, data lakes often provide better support. For controlled, consistent access for business intelligence, data warehouses are preferable.

Cost Analysis: Use analytics to forecast the costs associated with data storage, processing, and retrieval. Financial Decision: Data lakes can be more cost-effective for storing large quantities of raw data, whereas data warehouses may involve higher costs due to their complex infrastructure and data processing capabilities.

Data Lake and Warehouse are 2 different things and it all depends on what your use case is. A Data Lake is a pool of structured and unstructured data, like a vast library, it has a lot of raw data lying there but no specific objective yet. You can fetch the data from there on the basis of your need and process it and finally use it may be for ML, BI, AI stuff A Data Warehouse on the other hand is structured and has data with some purpose. The data would be well defined here and could be used directly or with minimal transformations primarily for reporting but also for other Analytics purposes like ML/AI implementation. So in a nutshell, it all depends what your end objective is

La analítica de datos es crucial al decidir entre un lago de datos y un almacén de datos. Los lagos de datos permiten almacenar datos sin procesar en su formato original, ideales para análisis avanzados y exploratorios mediante herramientas de big data y machine learning. Ofrecen flexibilidad y escalabilidad, cruciales para analizar grandes volúmenes de datos no estructurados y semiestructurados. Por otro lado, los almacenes de datos están optimizados para consultas rápidas y reportes estructurados, ideales para análisis operacionales y de negocio. Al elegir, la analítica de datos guía la decisión según el tipo de datos, los requerimientos de procesamiento, y la necesidad de agilidad y precisión en los análisis.

Data Lake: Ideal for exploratory analysis, machine learning, and handling diverse data types. Data analysts use it to discover hidden patterns in raw data. Data Warehouse: Best for reporting, BI dashboards, and answering specific business questions with structured data. Data analysts create reports and visualizations to track performance. Key factors in choosing: * Data types: Unstructured/semi-structured data favors a data lake. * Users: Business analysts needing reports prefer data warehouses; data scientists exploring raw data choose data lakes. * Budget: Data lakes are usually more cost-effective for large volumes. * Scalability: Both are scalable, but data lakes are more flexible for growing data.

Future Scalability: Consider future data needs and scalability. Data lakes are generally more adaptable to changes in data types and volumes. Integration Capabilities: Assess the integration capabilities with existing systems. Data warehouses often integrate better with traditional business applications, while data lakes offer flexibility for newer data-driven technologies. Regulatory Compliance: Determine which option better supports compliance with data governance and regulatory requirements, as data warehouses typically offer more robust tools for data lineage and auditing.