What are the latest trends in big data architecture patterns?

Trends may change but core skills remain same ,master these skills ????????????????????: - Batch Processing: Master tools like Spark . - Stream Processing: Flink and Spark for micro-batch streaming. ???????? ??????????????: - Databases: Understand SQL and NoSQL database, Distributed Storage - Data Warehouses: snowflake , redshift , big query ??????????????????: - Message Queues: Kafka is the go-to choice ?????????? ??????????????????????: - Cloud Services: Learn cloud offerings ???????????????? ????????????????????: - Workflows: Master Airflow for orchestrating, scheduling, and monitoring workflows. ???????? ??????????????????: - Event Data: Ingest and query massive event data with tools like Druid for low-latency insights.

Big Data Architecture Patterns are designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems. Some common patterns include: 1. Lambda Architecture: Combines batch and stream processing . 2. Kappa Architecture: A stream-only processing approach that treats batch data as a stream of events. 3. Microservices Architecture: Breaks down big data systems into smaller. Latest Trends: 1. Serverless: Run apps without managing servers 2. Data Mesh: Decentralized, domain-driven data management 3. Data Fabric: Integrated data across platforms 4. Hybrid/Multi-Cloud: Combining on-prem and multiple clouds 5. Real-time Stream Processing: Instant data processing for quick insights

Big data is designed to handle massive amounts of data that legacy systems cannot manage. Initially, big data were on-premises, requiring heavy and difficult-to-manage infrastructure. Cloud technology resolved these issues by making infrastructure management easier and scaling more cost-effective and time-efficient. Depending on requirement pure cloud or hybrid, you can adopt various techniques to handle the data. For instance: -Kappa Architecture: Focuses on streaming and serving layers to process real-time data. -Medallion Architecture: Organizes data into Bronze, Silver, and Gold layers to ensure data quality. Other architectures can also be chosen based on your specific needs, ensuring flexibility and efficiency in managing big data.

The demand for real-time analytics has driven the adoption of big data architecture patterns that support instantaneous data processing and analysis. Traditional batch processing is being supplemented or replaced by stream processing frameworks like Apache Kafka and Apache Flink, which enable continuous data ingestion, processing, and querying. This shift towards real-time analytics allows organizations to make data-driven decisions faster and respond rapidly to changing market conditions or emerging trends.

Real time Analytics is buzz words these days. when you are dealing with streaming data you need to think about Kappa Architecture first which is designed for streaming layers and serving layers, and improved version of lambda architecture.

You can assume data lake is centralize repository. Data lake allow you to store all kind of structure and unstructured data at any scale. Every data comes as form of raw data that we are calling dumping zone where raw data will be store and that could be in any format. Data Processing unit with the help of various tools like Databricks, Azure Fabric including different framework will process the data and create more meaningful. Final component is data catalog for data governance.

In my experience, data lakes is a cost effective way to store data. There are many challenges to make use of data lakes however, the top 2 being: 1. it's easy to turn data lakes into data swamps where you can't find the right/accurate/timely data since metadata management and lineage info are missing; 2. it's hard to govern the data across directories as well as databases/tables, meaning you will need to constantly sync your governance model across tables/directories/files... A lakehouse which combines the best of data lakes and data warehouse is a good solution in this case since it's built on top of cloud data lake and let you query and govern it like a data warehouse.

In my experience, this is easier said than done. Most organizations have strict rules about data access, and sending actual data to LLM will not be permitted under these rules. A good compromise is to only send the metadata to LLM to gain insight on the data and to support use cases such as text-2-sql.

AI-driven design is revolutionizing big data architectures, ushering in a new era of predictive analytics, anomaly detection, and automated decision-making. By integrating machine learning models into the data processing pipeline, organizations can extract deeper insights and enhance data quality. This transformative approach not only optimizes operations but also fuels innovation, empowering businesses to stay ahead in today's data-driven landscape. Embracing AI-driven design unlocks a world of possibilities, propelling organizations towards greater efficiency, agility, and competitiveness.

Absolutely! AI in big data architecture sounds super cool on paper - a smart assistant for your data! But in reality, it can be tricky. Many companies have tight rules about data access, and sending everything to some AI system might not fly. For me, a good middle ground is using AI on the data's "metadata" - like a detailed catalog of the data. This way, we can still get insights and maybe even translate stuff into code (like text-to-SQL) without compromising security.

In my experience below are the latest trends, Data Lakehouse architecture: combining the features of both data lake and data warehouse with open table formats like Hudi, Iceberg and Delta Lake Data Mesh architecture: Data mesh is decentralized approach focussed on the below key principals, - Domain oriented ownership - Data as a product - Self-service platform - Federated data governance Data Intelligence architecture incorporating Gen AI in the Data architecture from ingestion, processing, consumption and data management

A relatively new trend in big data architecture patterns is the adoption of the data mesh model. This approach decentralizes data ownership and management by treating data as a product and establishing domain-oriented data platforms. Each domain team is responsible for its data products, including ingestion, storage, processing, and serving, fostering a culture of data autonomy and accountability. By breaking down data silos and empowering domain experts, the data mesh model promotes agility, scalability, and innovation in data-driven organizations.

Latest Trends in Big Data Architecture Patterns Data Mesh: Decentralized data ownership. Data Lakehouse: Combines lake and warehouse. Real-Time Data: Real-time processing emphasis. Hybrid Cloud: Integrates on-premises and cloud. Edge Computing: Processing at data source. Data Fabric: Unified data management layer. AI Integration: Embedded AI for insights. Self-Service Analytics: Empowering non-tech users. These trends focus on scalability, flexibility, and real-time data insights.