A Comprehensive Guide to Data Lake Reference Architecture and Best Practices

A data lake is a centralized repository that stores raw, unprocessed data in its native format. This approach allows for greater flexibility and scalability compared to traditional data warehouses.

One of the key benefits of a data lake is its ability to handle large volumes of structured and unstructured data. According to the article, a data lake can store data in various formats such as CSV, JSON, and Avro.

Data lakes are often used for big data analytics and machine learning applications. They provide a single source of truth for all data, making it easier to integrate and analyze.

In a data lake architecture, data is typically ingested from various sources, such as IoT devices, social media, and databases.

A modern data lake is one-half data warehouse and one-half data lake, using object storage for everything. This approach provides all the benefits of object storage in terms of scalability and performance.

Organizations that adopt this approach pay only for what they need, facilitated by the scalability of object storage. They can also achieve performance by equipping their underlying object store with NVMe drives connected by a high-end network.

The rise of open table formats (OTFs) like Apache Iceberg, Apache Hudi, and Delta Lake has made it possible to use object storage as the underlying storage solution for a data warehouse. These specifications provide features that may not exist in a conventional data warehouse.

A modern data lake contains a data lake for unstructured data, which can be integrated with external data using OTFs. This integration allows external data to be used as an SQL table if needed, or transformed and routed to the data warehouse.

Collectively, a modern data lake provides more value than what's found in a conventional data warehouse or a standalone data lake. It offers scalability, performance, and features like snapshots, schema evolution, and zero-copy branching.

A data lake architecture is a framework for organizing and managing large amounts of data in a way that makes it easily accessible and usable for various purposes.

The modern data lake architecture is often divided into five main areas: Core Components, Data Acquisition, Data Engineering, Data Science, and Advanced Analytics & BI.

Each layer of the data lake has its own specific functions, including the ingestion layer, which contains the services needed to receive data, and the storage layer, which is typically object storage.

The processing layer contains the compute needed for all the workloads supported by the modern data lake, including data warehouse processing engines and clusters for distributed machine learning.

Here are the five layers of the modern data lake, from top to bottom:

The reference architecture for constructing a successful data lake can be divided into five main areas, including Data Acquisition, which involves bringing data into the data lake, and Data Science, which involves using data to train machine learning models.

Data can be ingested into the data lake via batch or streaming, and it's typically stored in the cloud storage system where ETL pipelines use the medallion architecture to store data in a curated way as Delta files/tables.

The Databricks lakehouse uses its engines Apache Spark and Photon for all transformations and queries, and it provides a declarative framework DLT (Delta Live Tables) for building reliable, maintainable, and testable data processing pipelines.

The data storage layer is the foundation of a modern data lake, responsible for storing data reliably and serving it efficiently. It contains separate object storage services for the data lake and data warehouse sides.

These two object storage services can be combined into one physical instance of an object store if needed, using buckets to keep data warehouse storage separate from data lake storage. However, consider keeping them separate and installed on different hardware if the processing layer will be putting different workloads on these two storage services.

A common data flow is to have all new data land in the data lake, where it can be transformed and ingested into the data warehouse. This data flow puts more load on the data warehouse, so it's recommended to run it on high-end hardware.

External table functionality allows data warehouses and processing engines to read objects in the data lake as if they were SQL tables. This capability can be used to transform raw data before inserting it into the data warehouse, or to join with other tables and resources inside the data warehouse without moving the data.

Most MLOP tools use a combination of an object store and a relational database to support MLOps. Models and datasets should be stored in the data lake, while metrics and hyperparameters will be more efficiently stored in a relational database.

The processing layer is where all the heavy lifting happens in a data lake, supporting a wide range of workloads.

It contains the compute needed for all workloads, coming in two varieties: processing engines for the data warehouse and clusters for distributed machine learning.

The data warehouse processing engine supports the distributed execution of SQL commands against the data in data warehouse storage.

Transformations during ingestion may also require the processing layer's compute power, especially for complex designs like medallion architecture or star schema with dimensional tables.

These designs often need substantial extract, transform, and load (ETL) against raw data during ingestion.

In a modern data lake, compute is disaggregated from storage, allowing multiple processing engines to exist for a single data warehouse data store.

This differs from a conventional relational database, where compute and storage are tightly coupled.

A possible processing layer design is to set up one processing engine for each entity in the consumption layer, ensuring teams don't compete for compute resources.

For instance, a business intelligence team might have one cluster, while a data analytics team has another, and a data science team has yet another.

Each team can query the same data warehouse storage service without interfering with each other's workloads.

Machine learning models, especially large language models, can be trained faster in a distributed fashion, utilizing the machine learning cluster for distributed training.

Distributed training should be integrated with an MLOps tool for experiment tracking and checkpointing.

Building a solid foundation is crucial for a data lake architecture to be effective, scalable, and maintainable. Establishing a robust platform foundation ensures that your data lake can handle the demands of large-scale data processing and analytics.

Cloud-based object storage services like Amazon S3, Azure Blob Storage, or Google Cloud Storage are popular choices for Cloud-based data lakes, providing scalable and durable storage options with pay-as-you-go pricing. These services allow you to scale up or down based on demand.

You can also provision virtual machines (VMs) or use managed services like AWS Elastic Compute Cloud (EC2), Azure Virtual Machines, or Google Compute Engine to deploy your data lake on Cloud. These services provide scalable compute resources that allow you to scale up or down based on demand.

Batch ETL is a crucial part of any data pipeline, and Databricks has made it incredibly efficient.

Ingest tools use source-specific adapters to read data from the source, which can then be stored in cloud storage for later use.

By using Databricks, you can run queries directly on the data, eliminating the need for intermediate storage.

Databricks Jobs can orchestrate single or multitask workflows, making it easy to manage complex data pipelines.

Unity Catalog provides access control, audit, lineage, and more, giving you a clear view of your data and its history.

At the end of the ETL pipeline, you can export specific golden tables to an operational database, such as an RDBMS or key-value store, for low-latency access.

To establish a solid platform foundation for your data lake architecture, consider starting with a scalable compute resource. You can provision virtual machines or use managed services like AWS Elastic Compute Cloud (EC2) or Google Compute Engine.

Cloud-based object storage services like Amazon S3 or Google Cloud Storage are popular choices for data lakes, offering scalable and durable storage options with pay-as-you-go pricing. These services are designed to handle large amounts of data and provide a cost-effective solution.

A pre-configured image with a supported operating system is available on cloud platforms, making it easier to set up your data lake. You can choose the operating system that best fits your needs and is supported by the cloud provider.

Cloud providers offer networking services to establish connectivity within the data lake, allowing you to configure virtual networks, subnets, security groups, and network access control lists (ACLs) to secure communication between different components. Consider using VPN or Direct Connect services to establish secure connections between on-premises and cloud environments.

Data Ingestion and Acquisition is a crucial step in setting up a data lake. Data is ingested from various sources such as transactional databases, logs, social media, and external APIs.

Consider using tools like Apache Kafka or Apache Nifi to collect and process data in real-time or batch mode efficiently. This helps ensure that your data is processed quickly and accurately.

Data lakes typically use distributed file systems like Hadoop Distributed File System (HDFS) or Cloud-based storage like Amazon S3 or Azure Data Lake Storage. These systems provide scalable and cost-effective storage for large volumes of data.

In a data lake reference architecture, analytics and business intelligence (BI) tools play a crucial role in enabling self-service data exploration, visualization, and reporting.

Analytics and BI tools can be connected to the data lake to provide business analysts with the ability to use the Databricks SQL editor or specific BI tools like Tableau or Looker.

To enable self-service data exploration, popular tools such as Tableau, Power BI, QlikView, or custom-built dashboards using frameworks like Apache Superset or Redash can be used.

Business analysts can use the Databricks SQL editor or specific BI tools like Tableau or Looker for BI use cases, and the engine is always Databricks SQL (serverless or non-serverless).

Data discovery, exploration, and access control are provided by Unity Catalog, making it easy to manage and secure data in the data lake.

The Modern Datalake must provide robust security measures to protect sensitive data. This includes authentication and authorization for users and services, as well as encryption for data at rest and in motion.

To achieve this, the Modern Datalake should support an Identity and Access Management (IAM) solution that facilitates authentication and authorization. Both the Data Lake and the Data Warehouse should use the same directory service for keeping track of users and groups, allowing users to present their corporate credentials when signing in.

The Modern Datalake uses a Key Management Server (KMS) for security at rest and in transit. A KMS generates, distributes, and manages cryptographic keys used for encryption and decryption.

Here's a quick rundown of the security features:

The Modern Datalake must provide authentication and authorization for users and services. It should also provide encryption for data at rest and data in motion. Both the Data Lake and the Data Warehouse must support an Identity and Access Management (IAM) solution that facilitates authentication and authorization. This solution should use the same directory service for keeping track of users and groups, allowing users to present their corporate credentials when signing into the user interface for both the Data Lake and the Data Warehouse.

To ensure secure programmatic access, the Data Lake requires a valid access key and secret key for each service wishing to access an administrative API or an S3 API. This includes PUT, GET, and DELETE operations. The Data Warehouse, on the other hand, needs an access token for programmatic access via ODBC connection, JDBC connection, or REST session.

The Data Lake should integrate with the organization's identity provider for authenticating users. This integration is crucial for ensuring that users are verified before accessing the Data Lake. By default, MinIO denies access to actions or resources not explicitly referenced in a user’s assigned or inherited policies.

A Key Management Server (KMS) is used for security at rest and in transit in the Modern Datalake. This server is responsible for generating, distributing, and managing cryptographic keys used for encryption and decryption.

ACID compliance is a must-have for some organizations, guaranteeing transactional consistency.

ACID stands for Atomicity, Consistency, Isolation, and Durability, ensuring that database transactions are processed reliably.

Delta Lake provides ACID compliance, allowing you to store and manage data in a way that meets strict compliance requirements.

In-Memory datasets are also available, providing fast and efficient access to data.

Dremio is a great tool for providing In-Memory datasets, allowing you to query and analyze data in real-time.

NoSQL databases offer flexible schema designs and high scalability, making them suitable for big data and real-time web applications.

Here are some popular NoSQL databases: