What is the most cost-effective data platform for data collection?

Na minha opini?o, isso depende muito dos objetivos e requisitos do negócio. Devemos projetar arquiteturas econ?micas que atendam ao cenário atual e também de longo prazo sempre se conectando ao planejamento estratégico e sendo aderente às opera??es da institui??o. Gosto muito dos servi?os oferecidos pela AWS, Azure e Google Cloud como por exemplo o AWS Glue, Azure Data Factory, Azure Synapse Pipelines e Google Cloud Data Fusion que auxiliam bastante na integra??o e extra??o de dados. Servi?os gerenciados, serverless, seguros e totalmente integrados que permitem a constru??o de projetos de dados eficientes.

Cloud-based, On-premise and Hybrid Holistic are for resolving the problems of people using that data for operation or analytics. We have to understand that either of those solutions has pros and cons and the decision should be made after consideration addressed. From 2024, I believe the question of what is the data strategy of a company and how data governance will help. Through the answer, we can start to think about which technical solution/provider/supplier we need to go forward.

Data Collection is Important aspect based on business requirement. Data collection would follow the important key rules as like follows. 1. Data classification :What is the actual data which we are receiving and is it PII related data or non-sensitive data? 2. Data Security: While processing the data we need to make sure data has been secured within our application if its sensitive data and for non-sensitive data also would follow the security related stuff and we need to build this in our application 3. Data platform depends upon the based on above steps since without these consideration we cannot choose the data platform app. 4.Post the above steps we need to collect the data and provide the expected results based on business requirement.

Each type of data platform comes with its own cost structure. On-premise solutions require substantial upfront investment in hardware and software, plus ongoing costs for maintenance and upgrades. Cloud-based platforms, on the other hand, operate on a subscription model, which can significantly reduce upfront costs and offer scalability. Hybrid models allow for a tailored approach that can optimize costs based on specific needs. Also, choosing the right platform ensures that the data strategy aligns with the broader business goals, whether that's maximizing security, enhancing flexibility, or optimizing costs.

In my experience, Cloud data platforms offer below benefits * Ease of data onboarding and Ingestion * Inbuilt data management, data life cycle management * Data reading and transformation using generic languages such as SQL * Data Governance has advanced significantly on major platforms like AWS, GCP and Azure

I believe a lot of times data platforms choices are just not based on cost but data security also plays a pivotal role. With the recent advancements and security practices being implemented in cloud we are seeing a paradigm shift where many such use-cases are moving to cloud, however there are still considerable chunks of critical financial systems who plan to hold their data onto their on-premise systems.

Choosing Data platform would depend on below factors: 1. Your Data Collection Needs: Data Size: Do you collect small, medium, or large volumes of data? Data Sources: Are you collecting from websites, APIs, sensors, or internal databases? Data Frequency: How often do you need to collect data (hourly, daily, weekly)? Data Complexity: Is your data structured (like a database) or unstructured (like social media posts)? 2. Your Budget: How much are you willing to spend upfront on setup and licensing? How much ongoing cost can you sustain for storage, processing, and maintenance?

The most cost-effective data platform for data collection depends on various factors such as the scale of data, types of data sources, required data processing capabilities, integration with existing systems, and budget constraints. Some popular cost-effective options include open-source platforms like Apache Kafka, Apache Hadoop, Apache Spark, and cloud-based solutions like AWS (Amazon Web Services) S3, Google Cloud Storage, and Azure Blob Storage. Each platform has its strengths and cost considerations, so it's essential to evaluate based on your specific needs.

The platform should support advanced analytics, machine learning, and data visualization tools that can extract valuable insights from the collected data. The ability to easily analyze and turn data into actionable intelligence is a key factor in platform selection.

While cloud-based platforms offer scalability and cost-effectiveness, it's important to consider the long-term implications of data gravity—the idea that once your data is in a particular ecosystem, it becomes harder and more expensive to move. This can lock organizations into a single cloud provider, potentially leading to increased costs and limited flexibility in the future.

One significant advantage of Delta Lake is its improved reliability. By enforcing data integrity and consistency, Delta Lake reduces the risk of errors or inconsistencies in stored data. Delta Lake contains features for ACID transactions, which ensure that data operations are performed reliably and securely. Furthermore, Delta Lake provides data versioning capabilities and this enables organizations to track and manage different versions of their data. However, implementing and managing Delta Lake may present some challenges. One consideration is the increased complexity associated with the platform. Additionally, there may be a performance overhead with Delta Lake compared to traditional data lakes.

Otro factor importante que he empezado a ver de manera común, es que hoy en dia a las empresas les interesa aprovechar su informacion de manera rapida, ciertamente la empresas no viven de tener sus datos bonitos y bien gobernados, si no mas bien de generar insights a través de ellos, por lo que muchas tecnologias recientes como el Modern Data Stack estan tratando empaquetar soluciones mas completas para un desarrollo rapido, en el que nos facilitan integraciones, conexiones, orquestaciones (onpremise y cloud), etc y todo dentro de una misma solucion que puede ser desarrollada por poco personal a un tiempo menor.

Choosing the most cost-effective data platform for data collection requires careful consideration of various factors to ensure optimal performance within a limited budget. Cloud-based solutions: Consider AWS, Azure, Snowflake or Databricks for scalable and flexible data collection at affordable prices. Open source tools: Explore Apache Kafka or Apache NiFi for robust data collection capabilities with minimal upfront costs. Vendor-specific offerings: Check out platforms like Salesforce or Adobe Analytics and weigh the benefits against potential fees and customization costs. Hybrid solutions: Choose hybrid approaches that combine on-premises and cloud-based solutions to balance cost efficiency with performance and security requirements.

From a broader perspective, the choice between data warehouses and data lakes is not just a technical decision, but a strategic one that should align with an organization's overall data strategy. Data warehouses, with their structured nature, are best suited for organizations that have established data processes and require fast, reliable reporting and analytics. They are traditionally the backbone of business intelligence applications.Data lakes, on the other hand, offer more flexibility and are suitable for organizations that need to store vast amounts of raw data, perhaps from IoT devices or streaming services, and want to keep their options open for the types of processing and analysis they might perform in the future.

Data Platform Options either on Cloud or on premises you have few options like OpenSource ecosystem: Where you can see Kubernetes, Kafka, Redis, Mongodb, S3, Hadop etc which provide their software with all features to Open source which is cost effective if your Data platform is Peta Bytes in scale and Analytics is part of the regular share of Compute. If you Choose Open source platforms you need to invest in High Skill engineering teams. Bespoke/Vendor sp. Tools: You get Cloud Native platforms like Snowflake, Databricks which would need less complex set up and straight out of the box features. If your existing set up is already in one Cloud platform you can use AWS/Azure/GCP sp datawarehouring or Analytics tools of the cloud.

It is important to have an evaluation of the data platform. To have a smooth operation in the organization with less budget keeping an evaluation will help the stakeholders to get an idea of the performance and efficiency of the platform.

It's essential to recognize that the evaluation of data platforms must be dynamic and take into account the evolving nature of data itself and the shifting landscapes of industries. The Gartner model and SWOT analysis provide frameworks that can help organizations align their data strategy with their broader business objectives. This ensures that investments in data platforms drive value and support the organization's vision and competitive positioning in the market.

Na avalia??o de uma plataforma de dados, é essencial considerar o desempenho, escalabilidade, facilidade de uso, integra??o, seguran?a, custo, suporte técnico, recursos de análise, flexibilidade e experiência do usuário. Esses critérios ajudam a determinar a adequa??o da plataforma às necessidades da organiza??o e a tomar uma decis?o informada.

You should consider factors like objectives, riska, cost, ease of integration and most important - scalability. You dont want to find yourself switching platforms often. Also, do some testing and validation before your final decision.

> When choosing a platform, consider factors like ease of integration with your existing infrastructure, scalability, community support, and the specific features offered by each platform. > Additionally, calculate the total cost of ownership, including not just the platform itself but also associated costs like storage, data transfer, and maintenance. > The most cost-effective platform for data collection will depend on your unique use case and requirements. > The choice of a cost-effective data platform for data collection in data engineering depends on various factors, including the specific requirements of your project, scalability needs, and preferred technologies.

The selection of a data platform should not be seen as an isolated IT decision but as part of a broader digital transformation strategy. The platform must align with the organization's data culture, readiness for change, and overall business strategy. It's not just about the technology itself but how it fits into the organization's processes, people's skills, and long-term vision.

The best ways to secure data centers include implementing robust access controls, encryption for data in transit and at rest, regular security audits, intrusion detection systems, physical security measures, and keeping software and systems up-to-date with patches and updates.

A sele??o de uma plataforma de dados envolve considerar vários fatores, como desempenho, facilidade de uso, integra??o, seguran?a, custo e suporte técnico. Além disso, é importante avaliar os recursos de análise, flexibilidade e experiência do usuário. Ao considerar esses aspectos, as organiza??es podem tomar uma decis?o informada que atenda às suas necessidades e objetivos de negócios.

Several factors to consider: Scalability: make sure the platform you choose can handle growing data volumes without significant cost increases. Integration: Evaluate how well the platform integrates with existing systems and tools to optimize data collection processes. Data security: Look for platforms with robust security features to protect sensitive data and comply with regulations. Support and maintenance: Consider ongoing support and maintenance costs, including training and updates, to avoid unexpected expenses. Future requirements: Plan ahead for future data collection needs and choose a platform that can adapt and scale accordingly.

As an organization, the prime goal is to cut operational costs with higher efficiency. For this scalability of the platform, the security of the data becomes the top priority. As for the next step platform should facilitate the requirements for data collection in the future run.