Hey, TechFlixers!
Software applications often produce billions of data points every day. The data collected and stored also scales rapidly as the number of users increases.
How exactly should we manage all this data?
The answer is in building a data lake— a centralized repository that allows you to store all your structured and unstructured data at any scale.
Everything related to your product—from user interactions and social media comments to reviews and in-app user-generated content, can all be put into a single place—the data lake.
In this episode, let’s explore how we can build and manage such a data lake with a real-world example. We will also explore some core concepts to understand data lakes better.
🔦 Spotlight
📢 Building and scaling a data lake at Notion
Notion built a scalable data lake using open-source tools like Debezium, Kafka, Deltastreamer, and Hudi to handle their rapidly growing data needs.
This system ingests changes from Postgres databases into Kafka, then writes them to S3 and a Hudi table format.
Spark is used for processing this data, taking advantage of its performance and multi-threading capabilities.
This setup allows Notion to process large datasets efficiently, saving money on storage and compute costs while reducing data ingestion time significantly.
The data lake has been instrumental in enabling new AI features and providing faster access to data for various analytics and product needs.
Let’s explore more on the fundamental concepts.
🚀 Concept Power Up
Key Concepts of Data Lake Architectures
1. Ingestion
Refers to the process of bringing data into the data lake. There are two primary ways to handle data ingestion:
Batch Processing: Involves collecting and processing data in chunks at scheduled intervals. For example, you might upload a batch of log files every night. Batch processing is useful for handling large volumes of data that do not need to be processed immediately.
Stream Processing: Involves processing data in real-time as it arrives. Think of user clicks on a website being captured and analyzed instantaneously. Tools like Apache Kafka are popular for stream processing due to their ability to handle high-throughput data streams efficiently.
2. Storage
Storage in a data lake is designed to be highly scalable and flexible.
Scalability: Data lakes leverage cloud storage solutions like AWS S3, Azure Data Lake Storage, or Google Cloud Storage, which offer virtually unlimited storage capacity.
Data Formats: A data lake can store data in various formats such as JSON, Parquet, Avro, or even raw text files. This flexibility allows you to choose the most efficient format for your specific needs.
3. Organization
Metadata Management: Metadata is data about data. It includes information like the data’s source, its format, and how it should be used. Proper metadata management is crucial for finding and managing data efficiently. Tools like Apache Atlas or AWS Glue can help catalog and maintain metadata, making it easier to search and utilize data.
Data Partitioning: This technique involves dividing data into smaller, more manageable parts (partitions) based on specific criteria like date or user ID. Partitioning can significantly improve query performance and data management by reducing the amount of data that needs to be scanned during queries.
4. Processing
Processing refers to transforming raw data into meaningful insights. This can be achieved through:
Data Processing Engines: Engines like Apache Spark, Apache Flink, and Presto are commonly used to process and analyze large datasets stored in data lakes. These tools are designed to handle big data efficiently, leveraging parallel processing to speed up computations.
ETL Pipelines: Extract, Transform, Load (ETL) pipelines are essential for cleaning, transforming, and preparing data for analysis. These pipelines ensure that data is in the right format and quality for downstream applications.
5. Security and Governance
Access Control: Implementing robust access control policies to ensure that only authorized users can access sensitive data. This can be managed through role-based access control (RBAC) systems and encryption.
Compliance: Ensuring that data governance practices comply with regulations like GDPR, CCPA, etc., to protect user privacy and maintain data integrity.
6. Consumption
Data Analytics and BI: Business Intelligence (BI) tools like Tableau, Power BI, and AWS QuickSight enable business users to create reports and dashboards from data stored in lakes. These tools help transform raw data into actionable insights.
Machine Learning: Data lakes are foundational for building and training machine learning models. Frameworks like TensorFlow, PyTorch, and AWS SageMaker can be used to develop and deploy models that leverage large datasets stored in the lake.
Design Patterns and Architectures for Data Lakes
Lambda Architecture
Combines batch and real-time processing to provide a comprehensive data processing framework:
Batch Layer: Stores all the data in its raw form and processes it in batches to provide accurate results.
Speed Layer: Processes data in real-time to provide immediate insights.
Serving Layer: Combines the results from the batch and speed layers to provide a unified view.
This architecture ensures that data is available for real-time analytics while maintaining accuracy through batch processing.
Kappa Architecture
Simplifies Lambda architecture by handling all data as a stream:
Stream Processing: All data is processed in real-time using a streaming platform like Kafka.
Unified Processing: Eliminates the need for separate batch and stream layers, simplifying the architecture.
This architecture is suitable for scenarios where real-time data processing is critical.
Data Vault
Focuses on modeling the data warehouse in a way that is flexible and scalable:
Raw Data Storage: Stores raw data in a central repository.
Business Logic Separation: Separates the raw data from the business logic, ensuring that historical data remains unchanged.
Scalability: Allows for incremental changes and scalability, making it ideal for complex data environments.
Event-Driven Data Architecture
Event-Driven Data Architecture uses events to trigger data processing and movement:
Event Streams: Utilizes tools like Kafka to handle real-time event streams.
Microservices Integration: Integrates well with microservices architectures and distributed systems.
Real-Time Processing: Allows for real-time data processing and analytics, enabling immediate insights and actions.
Job Titles Involving Data Lakes
Data Engineer
Designs, builds, and maintains data pipelines.
Ensures efficient data ingestion, storage, and processing.
Works with tools like Apache Spark, Kafka, and cloud storage solutions.
Data Architect
Designs the overall data lake architecture.
Ensures scalability, reliability, and security of the data lake.
Collaborates with stakeholders to define data requirements and governance policies.
Data Scientist
Analyzes and interprets complex data from the data lake.
Builds machine learning models and algorithms.
Utilizes data lake infrastructure to access and process large datasets.
Machine Learning Engineer
Develops and deploys machine learning models.
Integrates models with data pipelines and production systems.
Optimizes models for performance and scalability using data lakes.
Business Intelligence (BI) Developer:
Creates reports and dashboards using data from the data lake.
Transforms raw data into actionable insights for business users.
Works with BI tools like Tableau, Power BI, and AWS QuickSight.