What do you do if you're a Data Architect and need to handle big data in the future?

To manage big data well as a Data Architect, focus on using flexible and powerful systems, like cloud tools and advanced software that can handle a lot of information. Keep your data safe and organized, and always be ready to learn about new ways to make everything run smoothly and efficiently.

when I first started exploring the world of data: the power of the cloud. Imagine you've got this massive pile of legos, but instead of a solid table to build on, you've only got a tiny nightstand. That's what trying to manage big data without the cloud feels like. Now, the cloud? It's like having an ever-expanding table that grows in size whenever you need more space for your lego masterpiece. As a Data Architect, getting to know different cloud platforms is like learning to navigate in an infinite lego world. You can build castles (or databases) as big as you want without worrying about running out of space or the table breaking under the weight.

As a future data architect dealing with big data, I will prioritize scalability, flexibility and efficiency. Leveraging distributed computing frameworks such as Hadoop or Spark, I create data pipelines for ingestion, storage, processing, and analysis. I will ensure appropriate data management, security and compliance using cloud-based solutions to meet potential and cost-effective outcomes. I am constantly developing methods and techniques to improve performance by using advanced analytics and machine learning techniques to gain insight. Collaborating with cross-functional teams and staying current on the latest technology is critical to changing the competitive landscape and improving business efficiency.

For Big Data deepen expertise in specific technologies like Spark and Hadoop. It's also important to adapt existing knowledge of data modeling and storage and processing strategies to the unique challenges of big data. The key to success remains in building scalable systems that can grow with organizations needs.

Big Data solutions need "beyond single computer" thinking. It should be a cloud and you have to think about what kind of data you are planning to work with. You should use the full benefits of segregated storage and computing. Let's think about storage first. The cornerstone is an Object Storage for raw and enriched data zones (bronze and silver layers). About serving data zone (golden layer) - you can use conventional data warehouses with warm computing, or switch to more modern tech - Databricks Delta Lake, where data attached to the cold compute cluster for cost optimization. About Compute - there are a lot of Spark and Flink options available to work with your storage at every data zone.

This involves incorporating encryption techniques, stringent access controls, and regular security audits to safeguard sensitive information against breaches and unauthorized access. Ex. Designing secure big data architectures. You can implement encryption (at rest/in transit), access controls (RBAC, MFA), and data anonymization for sensitive data protection. You actively stay updated on security trends and regulations (GDPR, CCPA, DPDP) to ensure compliance.

Mostly you will think about speed and cost optimisation. Let me give some examples related to Object Storage - the most favorite choice for raw and enriched data zones, and often for serving zone too. About minimizing latency - you will most probably think about Object Storage replication options, like AWS S3 Cross-Region Replication (CRR). For cost optimization, you have to consider price/effort/benefits ratios for: - Public Cloud Object Storage like GCS, AWS S3 or ADLS2 - S3 compatible object storage inside cloud-managed Kubernetes cluster - S3 compatible object storage inside customer-managed Kubernetes cluster. For Public Cloud Object Storage, you have more options for cost saving like classes of storage and durability types.

In streaming data processes, robust tools and frameworks are vital for handling complex computations on large datasets. As a Data Architect, selecting and configuring these tools falls under your purview to guarantee smooth data processing. Become well-versed in distributed computing frameworks such as Apache Hadoop or Apache Spark, tailored for efficient processing across clusters of computers.

In handling big data in the future, Data Architects should focus on staying updated on technology trends, designing scalable architectures, and implementing robust data governance. Staying informed about advancements in big data technologies like distributed computing frameworks ensures efficient processing of large datasets. Designing scalable architectures enables the management of growing data volumes, ensuring system performance and reliability. Implementing robust data governance policies ensures data quality, security, and compliance with regulations, safeguarding sensitive information and maintaining trust with stakeholders

When handling big data projects, scalability is paramount. Your data architecture must accommodate the ever-growing volume of data. Plan for horizontal scalability by adding more machines and vertical scalability by enhancing existing ones. Designing with scalability in mind ensures your system can handle future data growth seamlessly, minimizing the need for major overhauls.

As a Data Architect planning for future big data needs, consider the below: - Update Skills: Constantly learn about new big data technologies and frameworks (e.g., Hadoop, Spark). - Scalable Architecture: Design systems to efficiently manage growing data volumes. - Leverage Cloud: Use cloud computing for flexible and scalable data storage and processing. - Data Governance: Ensure data integrity through robust governance and quality processes. - Utilize Analytics: Apply advanced analytics and machine learning for actionable insights. - Continuous Education: Stay updated with tools, languages (Python, Scala), and big data trends. - Collaborate: Work with teams to align data strategies with business objectives.

Big data is a relative term. What is big for one organization, let's say 100TB, may be small for another organization. YouTube uploads petabytes of data daily. I generally use the Large Hadron Collider as an example of big. Each experiment discards petabytes of information simply because it cannot store this at the time of the experiment. In fact, less than 1% of the data generated is collected, and 99% is lost. Handling big data will differ on the size, complexity and growth of the data set in question.