External Tables - A Comprehensive Guide

By: Oxana Urdaneta

As data practitioners, we constantly strive to optimize storing, managing, and processing data. One tool that has gained traction in modern data engineering is the concept of External Tables. But what exactly are they, why should you care, and how can they fit into your data strategy? Let’s dive in.

What Are External Tables?

External tables are a mechanism that allows you to query data stored outside of your database directly without the need to load it into your database storage first. Imagine your data residing in cloud storage services like AWS S3, Google Cloud Storage, or Azure Blob Storage—External Tables let you access and query this data as if it were part of your database (Snowflake, BigQuery, Redshift).

In essence, External Tables create a bridge between your database and external data sources. Instead of going through the traditional ETL (Extract, Transform, Load) process, where data is physically moved into your database, External Tables enable a streamlined ELT (Extract, Load, Transform) workflow where the data is transformed and queried on the fly.

In an external table, the table metadata (including schema and column definitions) is stored within your database, and the actual data remains in its original external location. The database query engine interacts with the external data source through this metadata, fetching and processing the data as needed. The opposite of this is Managed Tables, where both the metadata and the data are stored internally within your database.

Why Should You Care?

External Tables simplify data workflows and reduce costs:

Cost Efficiency: By querying data in place, you reduce the need to duplicate data in your database storage, leading to significant savings.
Data Freshness: Since you’re querying the data directly from its source, you can be confident that you’re always working with the most up-to-date information, minimizing latency.
Simplified Data Architecture: External Tables reduce the need for complex data pipelines that move and transform data multiple times, making your data architecture leaner and easier to manage.
Single Source of Truth: Having fewer copies of the same table decreases redundancy in your environment and enables a single source of truth for your data.
Increased Agility: Without loading data into your database before querying, you can quickly spin up new analytics and insights based on the latest data, adapting to business needs in real-time.

How to Use External Tables

Implementing External Tables varies depending on your database platform, but the general process is similar across the board:

Define the External Table: Specify the schema, including column definitions that match the structure of the external data source. The metadata resides within your database.
Point to the External Data Source: Define the location of the data, typically a file path in cloud storage (e.g., S3 or GCP). This step establishes the bridge between your table definition and the data files.
Query the Data: Once your External Table is set up, you can run SQL queries against it like a regular database table.

In databases like Snowflake and Redshift, External Tables often require the optimizer to make decisions based on metadata rather than actual data statistics, sometimes leading to less optimal execution plans. This is particularly important when working with large datasets where techniques like partition pruning and predicate pushdown can significantly impact performance.

Why Use External Tables?

Using External Tables often comes down to your specific data needs and architecture. Below are some key advantages and disadvantages:

Advantages	Disadvantages
Storage Savings: Since you're not duplicating data, you can save significantly on storage costs, especially when dealing with large datasets.	Processing Costs: While you save on storage, querying data directly from its source can be more expensive in terms of processing costs, especially for complex queries or large datasets.
Reduced Data Movement: Moving data is both time-consuming and error-prone. External Tables minimize data movement, reducing the potential for errors and the time needed for data to become actionable.	Performance Considerations: Depending on your query and the size of your dataset, performance may be slower than querying data already loaded into your database.
Increased Flexibility: External Tables allow you to access and query data from various sources, providing more flexibility in how you handle your data.	Dependency on External Systems: Your queries will be affected if the external data source is slow or unreliable.
Enhanced Data Quality: By querying data directly from its source, you're more likely to maintain high data quality, as there are fewer steps where data could be altered or corrupted.	Increased Data Governance complexity: Access controls must be managed adequately in your database and cloud storage provider.
Streamlined Workflows: With fewer steps in the data pipeline, External Tables can simplify workflows, making managing and scaling your data operations easier.

When to Use External Tables

External Tables shine in scenarios where data is large, dynamic, and doesn’t require heavy, complex transformations before analysis. They are particularly useful for:

Data Lakes: When working with raw, unstructured data stored in data lakes, External Tables allow you to query this data without first loading it into a structured database.
Data Sharing: External Tables provide a seamless way to access the same data without duplicating it if you need to share data across different teams or systems.
Hybrid Cloud Environments: For organizations utilizing both on-premises and cloud resources, External Tables can facilitate easier access to cloud-stored data without needing to move it on-premises.

When Not to Use External Tables

On the other hand, the following are cases when it is not recommended to use them:

High-Performance Use Cases: If your application or analytics process requires low-latency, high-speed access to data, relying on external tables might not meet performance expectations due to the added latency they introduce.
Frequent Queries: For workloads that require frequent querying or large-scale data processing, the performance overhead of external tables may outweigh their convenience. Local storage and traditional (managed) tables might be better suited for such use cases.
Frequent Data Access or Processing: If your workflows involve frequent reads or writes to the external data, the cost of repeatedly accessing this data can add up quickly, making storing it directly in your database more economical.

Final Thoughts

External Tables offer a powerful way to streamline data workflows, reduce storage costs, and maintain high data quality by enabling direct queries from external data sources. However, like any tool, they come with trade-offs, particularly in processing costs and performance.

Before implementing External Tables, consider your specific data needs and infrastructure. Are the storage savings worth the potential increase in processing costs? Is the performance hit acceptable for your use case? By carefully weighing these factors, you can determine whether External Tables are the right fit for your data strategy. Use them wisely; they can become a crucial component of your data engineering toolkit.