What is Data Lake?
Data Lake is a centralised storage repository that holds vast amounts of raw data in its native format until it is needed for analysis. Unlike traditional databases that require data to be structured before storage, a data lake accepts structured, semi-structured, and unstructured data, providing flexibility for diverse analytics use cases.
What is a Data Lake?
A Data Lake is a large-scale storage system designed to hold raw data in its original format. Rather than requiring data to be cleaned, structured, and organised before storage, as traditional databases do, a data lake accepts data as-is. This includes structured data from databases, semi-structured data like JSON and XML files, and unstructured data such as emails, documents, images, and video files.
The analogy is straightforward: a data lake is like a natural lake that receives water from many streams. The water (data) flows in from various sources and is stored in one place. When you need water for a specific purpose, you draw it out and process it as required.
How Data Lakes Work
A data lake architecture typically has several layers:
- Ingestion layer: Collects data from multiple sources, including databases, APIs, file systems, streaming platforms, and IoT devices. Data arrives in batch or real-time.
- Storage layer: Stores raw data in a cost-effective, scalable repository. Cloud object storage services like Amazon S3, Azure Data Lake Storage, or Google Cloud Storage are the most common choices.
- Processing layer: Transforms raw data into analysis-ready formats when needed. This is where tools like Apache Spark, Presto, or serverless query engines (e.g., Amazon Athena) come into play.
- Consumption layer: Provides access to processed data for analytics tools, machine learning models, dashboards, and business applications.
Data Lake vs Data Warehouse
This is one of the most common questions business leaders ask, and the distinction matters for investment decisions:
| Feature | Data Lake | Data Warehouse |
|---|---|---|
| Data format | Raw, any format | Structured, predefined schema |
| Cost | Lower storage costs | Higher per-unit cost |
| Flexibility | High, schema-on-read | Lower, schema-on-write |
| Query speed | Variable, depends on optimisation | Fast, optimised for queries |
| Best for | Exploration, ML, diverse data | Reporting, dashboards, known queries |
Many modern organisations use both: a data lake for raw data storage and exploration, and a data warehouse for curated, business-critical reporting. This pattern is sometimes called a data lakehouse.
Data Lakes in Southeast Asian Business
Data lakes are particularly valuable for companies operating across ASEAN markets because they can:
- Consolidate multi-market data: Store data from operations in Singapore, Indonesia, Thailand, Vietnam, and other markets in a single repository without requiring each market to standardise data formats first.
- Support diverse data types: Southeast Asian businesses often deal with data in multiple languages, character sets, and formats. Data lakes handle this diversity natively.
- Enable advanced analytics: Once data is centralised in a lake, data science teams can run machine learning models, customer segmentation analysis, and market research across the full dataset.
- Reduce costs: Cloud-based data lakes charge primarily for storage, which is inexpensive. You pay for processing power only when running queries or transformations.
Common Data Lake Pitfalls
Data lakes can become "data swamps" if not managed properly. Common mistakes include:
- No metadata management: Without a catalogue describing what data is stored, where it came from, and what it means, users cannot find or trust the data they need.
- Poor data governance: Allowing anyone to dump data into the lake without standards leads to duplication, inconsistency, and confusion.
- No access controls: Sensitive data (customer PII, financial records) mixed with general operational data creates compliance risks.
- Ignoring data quality: Raw data storage does not mean data quality does not matter. Garbage in still equals garbage out.
Building a Data Lake the Right Way
- Define a clear purpose: Know what business questions the data lake should help answer before building it.
- Implement a metadata catalogue: Use tools like AWS Glue Data Catalog or Apache Hive Metastore to track what data exists and what it means.
- Establish governance policies: Define who can write data, naming conventions, data retention policies, and access controls.
- Organise data into zones: Typically raw (landing), cleaned (curated), and consumption-ready (aggregated) zones.
- Start small and expand: Begin with two or three key data sources and add more as the organisation matures.
A data lake represents a strategic investment in your organisation's ability to use data flexibly and at scale. For companies that want to leverage AI and machine learning, a data lake is often a prerequisite, as these technologies require access to large, diverse datasets that traditional databases cannot easily accommodate.
In the Southeast Asian context, where businesses frequently operate across markets with different systems, languages, and data formats, a data lake provides the centralised foundation needed to build a unified view of operations and customers. Without this foundation, cross-market analytics and AI initiatives are severely constrained.
The financial argument is also compelling. Cloud-based data lakes offer dramatically lower storage costs compared to traditional data warehouses. For an SMB generating growing volumes of data, a data lake provides a cost-effective way to preserve and leverage data that might otherwise be discarded or siloed. The key is ensuring proper governance from the start to prevent the data lake from becoming an expensive, disorganised data swamp.
- A data lake is not a replacement for a data warehouse. Most organisations benefit from both, with the lake handling raw and diverse data while the warehouse serves structured reporting needs.
- Invest in metadata management and data cataloguing from day one. The value of a data lake depends entirely on users being able to find and understand the data it contains.
- Cloud object storage (S3, GCS, ADLS) is the most cost-effective and scalable foundation for a data lake. Avoid building on-premises unless regulatory requirements demand it.
- Establish clear data governance policies before opening the data lake to the organisation. Define ownership, quality standards, and access controls.
- Plan your data lake zones carefully: raw data landing, cleaned and validated, and consumption-ready layers prevent the lake from becoming a swamp.
- Consider data residency requirements in Southeast Asia. Some countries require certain data types to be stored within national borders.
Frequently Asked Questions
How much does a data lake cost for an SMB?
Cloud-based data lake storage is very affordable, typically a few cents per gigabyte per month. For an SMB storing a few terabytes, storage costs might be USD 50-200 per month. However, total costs include data processing (running queries and transformations), data ingestion tools, and personnel. A realistic budget for an SMB data lake including tools and part-time engineering support is USD 1,000-5,000 per month.
When should we choose a data lake over a data warehouse?
Choose a data lake when you need to store diverse data types (structured, semi-structured, unstructured), want flexibility to explore data without predefined schemas, or plan to run machine learning workloads. Choose a data warehouse when your primary need is fast, reliable reporting and dashboards on structured business data. Many organisations use both together.
More Questions
The three most important practices are: implementing a metadata catalogue so every dataset is documented and discoverable; enforcing governance policies that define data ownership, quality standards, and naming conventions; and organising the lake into clear zones (raw, cleaned, consumption-ready) with automated data quality checks at each stage.
Need help implementing Data Lake?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how data lake fits into your AI roadmap.