Big data platforms and tools can offer many benefits for data architects, such as scalability, flexibility, and performance. However, they can also pose some challenges, such as complexity, cost, and compatibility. How can you simplify your big data architecture and make the most of the available tools? Here are some strategies that can help you.
The first step to simplify your big data architecture is to choose the right platform for your needs. There are many options in the market, such as cloud-based, on-premise, or hybrid platforms, as well as different vendors, frameworks, and services. You should consider factors such as your data volume, variety, velocity, and value, as well as your budget, security, and governance requirements. You should also evaluate the features, functionality, and compatibility of each platform with your existing tools and systems.
Choosing "the right" big data platform is a real challenge since there is no "right" platform. There are bucket loads that can perform brilliantly given that you prepare your selected one in a good way. And this is where it's easy to fail.. First, you would want to consider the business requirements. Since there are usually no such ones, at least not useful, you'll have to make assumptions. When you have decided which one to go for, you need to invest a serious amount of hours to really learn the platform. This is where you are likely to fail. But those hours, and those skills, are much more important than the actual platform. Spark, just an example, can perform fantastic. Or just be very expensive, if your models are not good enough.
Existen varios puntos a tener en cuenta a la hora de decidirse ante una u otra plataforma. Entre ellos: 1. Entender las necesidades específicas del negocio. 2. Plataforma con escalabilidad horizontal. 3. Capacidad de integración de datos. 4. Capacidad y costo en almacenamiento de datos. 5. Procesamiento de datos alineado con los tipos de análisis que se realizarán. 6. Cumplimiento con las regulaciones de protección de datos. 7. Estándares abiertos e interoperabilidad con otras plataformas. 8. Implementación de políticas de gobernanza y calidad del dato. 9. Costo vs eficiencia. 10. Curva de aprendizaje, facilidad de uso y adopción.
Cloud vs onprem is the main distinction that needs to looked upon while platform selection . Building a good working infrastructure for a onprem big data system is much complex when compared to a cloud option . But if the data size is in very large number >100Tb and > 10Tb transaction per day might be cheaper to have a onprem system . Cloud big data set up is fast and much easier but can be costly with the relative increase in data size . Choosing the option in cloud varies slightly in cost but ideal selection will be by using cloud which supports the existing platforms and partnerships .
Choosing a right platform involves two important considerations for any organization. 1) Cost Effective 2) Data Security Either one of them compromises then non of the organizations choose that platform itself. So in my opinion the big data cloud providers azure/ AwS/ google cloud either of them are good to go. Now personal preference is Microsoft Fabric which overcomes lot of difficulties or challenges that this data analytics community facing unifying all data handling in a single umbrella that would solve most of the issues
Defining clear objectives guides tool selection. Choose user-friendly tools like Apache NiFi for streamlined data ingestion. Robust data catalogs and metadata management simplify asset tracking. Embrace automation tools such as Apache Airflow for workflow efficiency. Cloud services like AWS EMR abstract infrastructure complexities. Containerization, using Docker, ensures standardized deployment. User-friendly interfaces like Apache Zeppelin enhance interaction. Regular maintenance and team training foster platform simplicity. Collaboration and documentation promote knowledge sharing. These strategies collectively empower data architects to manage big data platforms effectively.
Another way to simplify your big data architecture is to use standard and open formats for your data. This can help you avoid vendor lock-in, reduce data silos, and facilitate data integration and interoperability. For example, you can use JSON, XML, CSV, or Parquet for your data files, and SQL, REST, or GraphQL for your data queries. You can also use open source tools and frameworks, such as Hadoop, Spark, Kafka, or TensorFlow, for your data processing and analysis.
Standard Operating Procedures SOP will be way to go. Making reusable code will also help improve productivity. Following industry standards will also go long way to integrating with other products.
File formats should make sense based upon usage and desired functionality. AVRO may be more suitable for machine learning workloads. Parquet for Delta Tables and transactional ACID compliance. Also be aware of how these file types contribute to your metadata collection strategy. For real time workflows Spark Structured Streaming offers sub second latency, but for true streaming opt for a framework like Flink.
Experienced Data Engineer || Data Scientist || ML Engineer || Azure AI Engineer
Step into the realm of seamless data collaboration — envision it as a shared conversation. In the current landscape, adopting standard and open data formats is transformative. It's about breaking down silos, creating a universal language across tools and platforms. Imagine liberating data from proprietary constraints, where widely accepted formats facilitate agile communication. Choosing open formats today isn't just a decision; it's about crafting a shared narrative in the ever-evolving story of data collaboration.
A third strategy to simplify your big data architecture is to automate and orchestrate your data workflows and tasks. This can help you save time, reduce errors, and improve efficiency and quality. For example, you can use tools such as Airflow, Luigi, or Nifi to schedule, monitor, and manage your data pipelines, and tools such as Ansible, Chef, or Puppet to automate your configuration and deployment. You can also use tools such as Terraform, CloudFormation, or Kubernetes to orchestrate your infrastructure and resources.
Use a visual data lineage monitoring tool. It could be a very effective way to intercept where data lose quality if not unxpected changes.
Experienced Data Engineer || Data Scientist || ML Engineer || Azure AI Engineer
In the current data-driven era, envision your workflows as a finely tuned digital dance. Automation and orchestration aren't just about reducing manual efforts; they're the conductors ensuring a symphony of data workflows. Picture a scenario where routine tasks seamlessly blend, optimizing efficiency and adapting to real-time demands. Today, automation and orchestration aren't just tools; they're the choreographers navigating the complexities of modern data processing, allowing organizations to dance gracefully through dynamic landscapes.
A fourth strategy to simplify your big data architecture is to document and test your data assets and processes. This can help you ensure consistency, accuracy, and reliability of your data, as well as facilitate collaboration and communication among your stakeholders. For example, you can use tools such as Data Catalog, Data Dictionary, or Data Lineage to document your data sources, schemas, metadata, and transformations. You can also use tools such as PyTest, JUnit, or Selenium to test your data quality, performance, and functionality.
Documenting and testing for big data involves several key steps to ensure the accuracy, reliability, and performance of the data processing and analysis. Here's a high-level overview of the process: 1. Data Documentation 2. Data Quality Testing 3. Performance Testing 4. Security and Privacy Testing 5. Integration Testing 6. Analysis and Reporting: It's important to note that the specific testing methodologies and tools may vary depending on the big data technologies and frameworks used in your environment.
Hypothesis library for property based testing, and Great Expectations library for validation, documentation, and quality checks.
Navigate the complexities of contemporary data reliability where comprehensive documentation and rigorous testing take center stage. Imagine it as the meticulously crafted script and dress rehearsal before the main act. In the current scenario, comprehensive documentation ensures a clear architectural storyline, facilitating collaboration in virtual spaces. Rigorous testing is the tech rehearsal, identifying potential hiccups in the era of agile, iterative development. It's not just about safeguarding against errors; it's about ensuring the reliability and resilience demanded by the fast-paced, data-driven performances of today.
A fifth strategy to simplify your big data architecture is to optimize and refine your data solutions and outcomes. This can help you enhance your data value, usability, and impact. For example, you can use tools such as Data Profiling, Data Cleansing, or Data Enrichment to improve your data quality, completeness, and relevance. You can also use tools such as Data Visualization, Data Analytics, or Data Science to extract insights, patterns, and predictions from your data.
In the current data landscape, optimization and refinement are the keystones to staying ahead. Visualize a big data platform as a living entity, evolving with each review and update. Today, regular refinements based on real-time requirements lead to enhanced performance and resource efficiency. It’s not just about choosing a solution; it’s about the agile evolution of your platform to meet the dynamic demands of contemporary data. Think of it as a continuous fine-tuning, ensuring your platform is not just current but anticipates the data needs of tomorrow, staying relevant in the ever-changing data scenario.
A sixth strategy to simplify your big data architecture is to learn and adapt to the changing needs and expectations of your data users and consumers. This can help you stay ahead of the curve, innovate, and deliver value. For example, you can use tools such as Feedback, Surveys, or Metrics to collect and analyze your data usage, satisfaction, and impact. You can also use tools such as Blogs, Podcasts, or Courses to keep up with the latest trends, best practices, and technologies in the big data field.
Find an active Slack or discord community and talk to like minded individuals. There's tons of public datasets and free cloud credits to undertake cool projects like CDC with Debezium. Open source tools will get you far.
Do we really need a big data architecture? Has the futuristic cost analysis been made using big data or cloud implementation ? For Example, a Teradata or old legacy systems can cost you 1 million per year for license and infrastructure. If your planning to move to cloud like snowflake / Azure databricks ? Have you actually calculated the over all cost of running the jobs 24/7 ? Will this actually save money or will make it more costlier ? Do we really have a such huge amount of data ??
As we navigate the intricacies of modern data ecosystems, effective collaboration and transparent communication stand as pillars of success. Envision projects flourishing with open channels among team members and stakeholders, fostering a culture of shared insights. Picture a landscape where data security isn't just a measure but an integral part of the narrative, ensuring compliance in an era of heightened awareness. Engage with the community for real-time insights, embracing cross-functional collaboration as a strategy to meet the diverse needs of a rapidly evolving business environment. Consider these elements not as mere considerations but as the essential ingredients crafting a narrative of success in today's data-driven conversations.
Develop a nuanced view of the business value that the organization wants to achieve with its big data program and use that assessment to guide an agile delivery of the needed technologies.