Why you need a modern data lake
Traditionally, data lakes were built on on-premises infrastructure, utilizing relational databases, file systems, or Hadoop-based frameworks for storage and batch processing. They acted as a centralized repository that was more cost-effective than data warehouses. They may have been designed to handle specific types of data or workloads, and they might lack flexibility and agility in accommodating evolving business requirements and new data sources.
These legacy data lakes often suffer from challenges such as scalability issues, lack of data governance and quality controls, limited analytics capabilities, and difficulties in integrating with newer systems and tools. This is often referred to as a data swamp.
Key components of a modern data lake architecture
To build a modern data lake, several essential components are required to ensure its effectiveness and efficiency. These components include:
- Commodity Storage and Compute: Modern data lakes are often built on cloud platforms that provide commodity storage and compute resources. For example, Amazon S3 (Simple Storage Service) or Elastic Compute Cloud (EC2) in AWS, or Azure Blob Storage and Azure Virtual Machines in Azure. These resources offer scalable, cost-effective, and durable storage and compute capabilities required for storing and processing large volumes of data (i.e. big data).
- Open File and Modern Table Formats: To maximize data accessibility and compatibility, modern data lakes support open file formats and modern table formats that provide additional capabilities beyond traditional file formats like CSV or JSON. Examples of modern table formats include Iceberg, Delta Lake, and Hudi. These formats offer features such as schema evolution, transactional consistency, ACID (Atomicity, Consistency, Isolation, Durability) properties, and time travel capabilities. They enhance data management, improve data quality, and enable efficient data processing and analytics workflows.
- High-Performance and Scalable Query Engine: A crucial component of a modern data lake is a high-performance and scalable query engine. It allows users to run fast and complex queries on large datasets stored in the lake. Popular query engines for data lakes include Apache Spark and Trino. These engines leverage distributed processing techniques to perform parallel and optimized query execution across a cluster of machines, enabling efficient data retrieval and analysis.
- Federated Access to External Data: In addition to the data residing in the data lake, organizations often have data distributed across various sources and systems. To provide comprehensive insights, a modern data lake should offer federated access to these external data sources. This involves integrating connectors and interfaces that allow querying and accessing data from databases, data warehouses, APIs, or other external systems. By providing a unified view of all data, regardless of its location, users can analyze and derive insights from diverse datasets within the data lake environment.
By incorporating these components, organizations can create a powerful and flexible data lake architecture that enables efficient data management, analytics, and decision-making.
Related reading: Designing a data lake analytics architecture for a startup
Advantages of a modern data lake architecture
Modern data lakes overcome the fundamental issues that have turned data lakes into data swamps.They bring quality to your data lake by adding key data warehousing capabilities such as transactions, schemas and governance. They also leverage various performance optimization techniques to enable fast data analytics.
Use cases for modern data lake
A modern data lake offers a wide range of use cases across different industries and domains. Here are some common use cases where a modern data lake can provide significant value:
- Data Exploration and Discovery: A modern data lake’s flexible schema-on-read approach allows users to explore and discover new insights from raw, unstructured, and semi-structured data. Data scientists, analysts, and business users can perform ad-hoc queries, data exploration, and interactive data visualizations.
- Data Engineering: Data lakes support data transformation and integration tasks, allowing data engineers to clean, enrich, and transform data within the lake. Data engineers can build ETL/ELT (Extract, Transform, Load) pipelines to perform data transformations, join multiple datasets, aggregate data, and apply business rules or data validation logic.
- Data Science and Machine Learning: Data lakes provide a rich environment for data scientists to access and analyze large volumes of data for building and training machine learning models. Data scientists can perform tasks such as predictive modeling, anomaly detection, customer segmentation, recommendation systems, and natural language processing using the diverse data available in the lake.
- Business Intelligence: Data lakes can serve as a scalable and cost-effective alternative to traditional data warehouses for storing and analyzing structured data. By integrating data lake technologies with business intelligence tools like Tableau, Power BI, or Looker, organizations can perform interactive reporting, dashboarding, and self-service analytics, enabling business users to access and analyze data with ease.
What is the difference between a modern data lake, a data lakehouse, and a modern data architecture?
Modern data lake, data lakehouse, and modern data architecture are mostly synonymous and describe a data management architecture that combines advanced data warehouse-like capabilities for scaling data analytics with the flexibility and cost-effectiveness of a data lake. Modern data lakes go one step further than data lakehouses by providing federated access to data around the lake, so you can explore your data in real-time before centralizing it.
Modern data lakes include a high performance query engine, open table formats, open file formats, governance layer as well as commodity object storage and elastic compute, particularly using cloud infrastructure (e.g. AWS S3, EC2, Google Cloud Storage, Azure Data Lake Storage).
How does Starburst help you build a modern data lake?
Starburst’s data lake analytics platform provides you with the tools you need to build a modern data lake.The data platform consists of four core layers:
- Data access layer for connecting to all your data sources in and around your lake. This also includes our unique Stargate connectivity, which allows customers to connect sources across regions, across clouds, and between cloud and on-prem environments.
- Security and governance layer which includes all the capabilities needed to manage access, privacy, and encryption, as well as things like monitoring and logging.
- The query engine, which for us is built on top of open source Trino (formerly known as Presto). We’ve added significant proprietary features here as well, including Warp Speed, which automates indexing and partitioning, allowing you to get query response times similar to an optimized data warehouse and explore your data in near real-time. Also as part of the engine, we offer fault-tolerant execution, which means long running queries and complex transformation jobs will not fail due to out-of-memory limitations.
- Finally, we have the modeling and semantic layer, which includes all the features to help you build, organize, and share data models with standard SQL.
How does Starburst improve data management?
Starburst’s data lake analytics platform abstracts data management and consumption away from the boundaries of your data architecture, freeing companies to connect to any storage layer, file format, and table format that meets the needs of a specific data workload without sacrificing organizational visibility or best-in-class governance practices.
Related reading: Data Lake Analytics for Smart, Modern Data Management
How to get started with Starburst
With Starburst, users can run ad-hoc and batch queries, build interactive dashboards and resilient data pipelines, and perform data exploration on a variety of data sources. The platform is designed to be scalable and can handle large and complex data sets that are distributed across multiple systems.
Starburst Galaxy
Starburst Galaxy is a fully managed data lake analytics platform designed for large and complex data sets, including raw data, semi-structured, and unstructured data—in and around your cloud data lake eliminating data silos. It is the easiest and fastest way for you to build a modern data lake and connects to the business intelligence and analytics tools you already know.
Related reading: Google Looker and Starburst Galaxy: Modern, trusted BI for your modern data lake
Related reading: Tableau and Starburst Announce New “One-Click” Connectivity Further Simplifying BI on the Modern Data Lake
Starburst Enterprise
Starburst Enterprise is a self-managed, enterprise-grade distribution of the popular open-source project Trino. Starburst Enterprise adds additional features, such as enterprise-grade security, access controls, a variety of supported connectors, improved performance, and a user-friendly interface.
