Designing a data lake and analytics architecture

5 layers of an open data lake analytics architecture

Last Updated: April 16, 2024

Of all the choices a startup has to make in its early stages, deciding on the right data analytics architecture might not seem critical, and the modern data stack certainly looks like the easiest route. You pick a cloud data warehouse, start streaming data, and choose your favorite BI or data science toolkit to uncover trends and insights. 

The problem with this approach is you commit your company to an analytics stack that isn’t designed to scale cost-effectively or adapt as your business changes. 

Cloud data warehouses tie you to their proprietary data formats and cost structures. As your organization grows, different teams or individuals might have different preferences for BI and data science tools that don’t play well with your chosen warehouse. If you acquire another company, you’ll have to find a way to efficiently merge your datasets. What you do not want to do, as this CIO article points out, is discard data for the sake of efficiency. 

Ideally you want to make all data about your business, operations, and customers available to your analytics, BI, and data science tools, and do so in a way that’s efficient, cost-effective, scalable, and preserves your optionality, or ability to switch solutions or providers down the road. 

How do you get this done inside a startup with limited resources and evolving priorities? This post details why an analytics stack built around open file and table formats, a distributed query engine, and the modern data lake is the ideal architectural choice for early-stage startups. 

1. Pick a data lake

A data lake allows you to cost-effectively store unlimited volumes of structured or unstructured data in one centralized repository. In the past, data lakes acquired a less than stellar reputation because you couldn’t layer advanced analytics on top, and the lakes devolved into swamps. 

Today, it’s possible to layer data-warehouse-like analytics capabilities on top of a data lake, so you can leverage both the cost savings of the cloud data lake and the fast analytics of the traditional warehouse. You can get the best of both worlds. 

2. Select an open file format

Once you’ve picked a data lake provider, you need to decide how you are going to store your data. 

The open file formats Parquet, ORC, and Avro have their differences, but they are designed for big data and analytics use cases. Since they are open source formats, you truly own your data, as you’re not subjected to vendor lock-in

When you stream your data into a traditional or cloud data warehouse, on the other hand, your data is converted into a proprietary format, which makes it that much harder to shift or migrate data to another provider in the future. This is a great business model for the warehouse, but not so much for the customer. 

As a startup, you want to preserve your options and agility going forward in case a competing provider offers better pricing, features, or benefits that match your business. Choosing an open file format is a critical step.

3. Find the right table format

Once you select an open file format, you need to select a table format to organize that data. 

The leading formats today are Apache Iceberg, Delta Lake, Apache Hudi and, to a lesser extent, Apache Hive. Choosing the right table format is just as important as selecting the optimal file format, and each table has its benefits. 

If you are really planning or hoping to grow, however, we would suggest Iceberg, as it is designed to serve as a high-performance format for very large datasets, and we’ve seen how efficiently it functions at scale. 

4. Embrace open source technologies

Open source technologies offer numerous advantages, including cost-effectiveness, community-driven innovation, and flexibility. 

They provide startups with limited resources, with greater agility and customization options, and transparency. Startups can build a robust, cost-effective, and adaptable data infrastructure that empowers them to extract meaningful insights and drive business growth. 

5. Choose an analytics engine

Next you’ll need to select an analytics engine to query this formatted data  and uncover insights, trends, and other information. Ideally, your analytics engine should be highly performant and efficient, but it should also be:

  • Scalable: The engine should be able to operate cost-effectively as your data lake grows or as you bring new datasets into the picture.
  • Flexible: Preserving optionality is critical – you want to avoid building your analytics stack around a query engine that ties you to a particular cloud and you should be able to use that analytics engine with a variety of BI and data science tools.
  • Future-proof: The engine should be able to adapt over time as you incorporate new data sources – a newly acquired company with its own warehouse, for example – or utilize additional BI and Data Science tools.

Things move quickly inside a startup, as we know from our own experience. It might not be clear at first which visualization, BI, or data science tools will win out as the ideal platforms for your organization. So you want to preserve that flexibility or optionality as you grow.  

How a modern data lake can unlock the full potential of your data

A modern data lake has emerged as an evolution of legacy solutions, addressing both challenges of cost and speed of data accessibility. Modern data lake platforms introduce changes to the data architecture that significantly enhance its capabilities.

A modern data lake is built on commodity storage and compute infrastructure. This ensures that startups can effortlessly scale their resources up or down in a cost-effective manner as their needs evolve. 

Secondly, it relies on open file and table formats, guaranteeing data portability and ownership. By using open formats, startups can prevent vendor lock-in and maintain control over their valuable data. 

Naturally, a high-performance and scalable query engine is crucial for efficiently querying the extensive amounts of data stored in the lake.

Additionally, the ability to access data beyond what resides in the lake is imperative. Startups should have the flexibility to integrate and analyze data from various sources, expanding their insights and possibilities. 

By embracing these advancements in the modern data lake architecture, organizations can unlock the full potential of their data, ensuring improved performance, governance, and accessibility for data-driven decision-making. 

Any startup is forced to make multiple difficult decisions on a regular basis, and designing the right data lake analytics architecture might not appear to be as critical as other early-stage decisions, but getting this architecture right early can pay tremendous dividends as your company grows. 

A cost-effective analytics engine for growing companies

Starburst is a data lake analytics platform that is an ideal analytics engine for startups building their own data lake architecture because it scales cost-effectively, preserves optionality, and adapts with startups as their needs and priorities shift over time. Built on the open-source Trino, the platform enables fast and scalable SQL queries across multiple data sources without requiring data movement or transformation and works seamlessly with multiple BI and data science tools. 

In simple terms, it’s a scalable, cost-effective analytics engine that has proven to be ideal for growing companies. 

A few high-level features include:

  • Activate the data in your lake, lakehouse, or warehouse anywhere — in the cloud, across clouds, on-prem, or hybrid
  • Smart indexing and caching accelerates interactive queries and dashboards by 40%+
  • Out-of-the-box data discovery, cataloging, data sharing, access management and security controls
  • Federated query capabilities for distributed datasets
  • Integration with popular BI tools and data science notebooks
  • Long-running workloads without the fear of query failure, even on limited hardware

By embracing a modern data lake architecture, startups can overcome data access challenges, improve query performance, enhance data governance, and simplify data consumption for business intelligence. 

As you embark on your data-driven journey, we encourage you to explore Starburst Galaxy, a comprehensive data lake platform that combines the power of Trino with a range of advanced features and integrations. 

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