Evolving the Data Lake: From CSV/JSON to Parquet to Apache Iceberg

As companies generate more and more data, the need for efficient storage and analytics solutions becomes increasingly important. This journey often begins with simple formats like CSV and JSON, which are accessible and easy to work with. However, as the volume and complexity of data grow, these formats become impractical for large-scale analytics, pushing companies to seek more advanced solutions. This blog explores the evolution of data storage formats, from the simplicity of CSV and JSON to the efficiency of Apache Parquet, and finally to the robust capabilities of Apache Iceberg. Along the way, we'll discuss how Dremio’s Lakehouse Platform supports this evolution, providing a unified solution that grows with your data needs.

The Starting Point – CSV and JSON

Introduction to CSV/JSON

CSV (Comma-Separated Values) and JSON (JavaScript Object Notation) are often the first formats that companies use to store and exchange data. These formats are ubiquitous because they are straightforward to generate and consume, making them ideal for small datasets and initial data storage needs.

CSV files organize data in a tabular format, with each line representing a row and each value separated by a comma.

id,name,email,age,city
1,John Doe,[email protected],29,New York
2,Jane Smith,[email protected],34,Los Angeles
3,Bob Johnson,[email protected],45,Chicago
4,Alice Williams,[email protected],28,Houston
5,Michael Brown,[email protected],37,Phoenix

JSON, on the other hand, structures data in a nested key-value format, which is particularly useful for representing more complex, hierarchical data.

[
  {
    "id": 1,
    "name": "John Doe",
    "email": "[email protected]",
    "age": 29,
    "city": "New York"
  },
  {
    "id": 2,
    "name": "Jane Smith",
    "email": "[email protected]",
    "age": 34,
    "city": "Los Angeles"
  },
  {
    "id": 3,
    "name": "Bob Johnson",
    "email": "[email protected]",
    "age": 45,
    "city": "Chicago"
  },
  {
    "id": 4,
    "name": "Alice Williams",
    "email": "[email protected]",
    "age": 28,
    "city": "Houston"
  },
  {
    "id": 5,
    "name": "Michael Brown",
    "email": "[email protected]",
    "age": 37,
    "city": "Phoenix"
  }
]

Both CSV and JSON are supported by a wide range of tools and programming languages, making them highly accessible for developers and data practitioners. Whether exporting data from a database, sharing data between systems, or storing configuration files, these formats are often the go-to choice for their simplicity and ease of use.

Limitations of CSV/JSON

While CSV and JSON are convenient for small-scale use cases, they come with several limitations that become apparent as data volumes grow. One of the main challenges with these formats is their lack of schema enforcement, which can lead to inconsistencies and errors when ingesting data into more structured environments like databases or data warehouses.

Additionally, as datasets grow in size, CSV and JSON files become increasingly inefficient. These text-based formats are not optimized for storage space, often resulting in large file sizes that are costly to store and slow to query. Moreover, their flat structure makes it difficult to perform complex analytical queries, particularly when dealing with large-scale data. As a result, companies often find that they need to move beyond CSV and JSON to more efficient formats that can handle the demands of modern data analytics.

The Next Step – Parquet

Introduction to Apache Parquet

As companies accumulate more data and their analytical needs become more sophisticated, the limitations of CSV and JSON formats start to hinder performance and scalability. This is where Apache Parquet comes into play. Parquet is a columnar storage format that was designed specifically to address the inefficiencies of row-based formats like CSV. Unlike CSV or JSON, where data is stored row by row, Parquet stores data by columns. This columnar structure offers significant advantages when it comes to analytical workloads.

One of the primary benefits of Parquet is its ability to compress data efficiently. Because data within a column tends to be similar, Parquet can apply more effective compression algorithms, resulting in smaller file sizes compared to CSV or JSON. This not only reduces storage costs but also speeds up data retrieval, as less data needs to be read from disk during queries.

Additionally, Parquet supports advanced features like schema evolution and data types, which enhance its compatibility with modern data processing frameworks. Schema evolution allows for the addition of new columns or changes to existing ones without breaking compatibility with previous data, making Parquet a flexible choice for growing datasets. Overall, Parquet is optimized for read-heavy workloads, making it a popular choice for companies looking to improve the efficiency of their data lakes.

Use Cases for Parquet

Many companies begin to transition from CSV or JSON to Parquet when they encounter performance bottlenecks in their analytics processes. As datasets grow larger, the time required to load and query data stored in text-based formats increases significantly. Parquet, with its columnar storage and compression capabilities, can drastically reduce query times, making it easier to analyze large datasets quickly.

For example, a company that initially stored user activity logs in JSON might find that querying this data to generate reports becomes sluggish as the logs grow into the terabyte range. By converting these logs into Parquet format, the company can significantly speed up the reporting process, as only the relevant columns (e.g., timestamps and user IDs) need to be read during the query, rather than the entire dataset.

Another common scenario involves the need for cost-efficient storage. As companies store more data, the cost of storage becomes a concern. Parquet’s compression can reduce the size of datasets by an order of magnitude, resulting in lower storage costs. This is particularly valuable for companies with large volumes of historical data that they need to retain for compliance or analytical purposes.

Apache Parquet is a natural progression for companies whose data needs have outgrown the capabilities of CSV and JSON. Its efficient storage and querying capabilities make it an ideal choice for large-scale data analytics, providing a more scalable and cost-effective solution as data volumes continue to increase.

The Final Evolution – Apache Iceberg

Introduction to Apache Iceberg

As companies continue to scale their data operations, even the benefits of columnar storage formats like Apache Parquet can start to show limitations. Large datasets often require more than just efficient storage; they need robust data management capabilities, such as support for complex transactional workloads, data versioning, and schema evolution without compromising performance. This is where Apache Iceberg comes into play—a powerful table format designed specifically for large-scale, high-performance analytics on data lakes.

Apache Iceberg builds on the strengths of Parquet and other columnar formats, but it goes several steps further by introducing a full-fledged table format that supports ACID (Atomicity, Consistency, Isolation, Durability) transactions, allowing for consistent and reliable data management. Iceberg also maintains a snapshot history, which enables users to "time travel" to previous versions of the dataset. This is particularly useful for auditability, debugging, and compliance, where historical data must be accurately preserved and queried.

Apache Iceberg is designed to handle schema evolution more gracefully than traditional formats. With Iceberg, changes to the schema, such as adding, dropping, or renaming columns, can be managed without requiring expensive and time-consuming rewrites of entire datasets. This flexibility makes Iceberg an ideal choice for dynamic environments where data structures are continually evolving.

Benefits of Apache Iceberg

Apache Iceberg transforms the concept of a data lake from a simple repository of files into a full-featured data warehouse that can handle the demands of modern analytics. One of the most significant benefits of Iceberg is its scalability. Iceberg can efficiently manage datasets that span petabytes or even exabytes of data, making it suitable for enterprises with massive data footprints.

The table format of Iceberg allows for sophisticated partitioning and indexing, enabling faster queries even as datasets grow. Unlike traditional approaches that rely solely on file-based partitioning, Iceberg’s approach minimizes the need for manual partition management, reducing the complexity and maintenance overhead associated with large-scale data lakes.

Another crucial benefit of Iceberg is its support for ACID transactions, which ensure data integrity and consistency across the entire dataset. This feature is vital for businesses that require reliable data operations, such as updating records or performing complex data merges, without risking data corruption or loss. ACID transactions in Iceberg allow organizations to maintain high levels of data quality, even in environments with frequent data updates and high concurrency.

Additionally, Iceberg’s integration with modern data processing engines like Apache Spark, Flink, and Dremio means that it can seamlessly fit into existing data ecosystems. This compatibility ensures that companies can leverage Iceberg’s advanced features without overhauling their entire infrastructure.

In summary, Apache Iceberg represents the final evolution in the journey from simple data storage formats to a fully-featured, scalable, and high-performance data management solution. By turning raw data files into a full-blown data warehouse, Iceberg enables organizations to tackle the most demanding analytical workloads with confidence, ensuring that their data lake is ready for the future.

The Role of Dremio in Your Data Journey

Introduction to the Dremio Lakehouse Platform

As companies navigate the evolving landscape of data storage formats—from CSV/JSON to Parquet and finally to Apache Iceberg—the need for a versatile and powerful data platform becomes apparent. Enter Dremio, a lakehouse platform that is uniquely equipped to handle this evolution. Dremio provides a unified solution that allows organizations to seamlessly query and manage their data, regardless of the format or scale. Whether you’re just starting with small CSV files or managing massive datasets in Iceberg, Dremio’s platform is designed to grow with your data needs.

Querying Across Data Formats with Dremio

One of the standout features of Dremio is its ability to query across multiple data formats, databases, data warehouses, Data Lakes and Lakehouse Catalogs without the need for complex data migrations or transformations. Dremio allows you to query CSV, JSON, Parquet, and Iceberg tables natively, enabling you to extract valuable insights from your data, no matter where you are in your data journey.

For instance, you might have legacy CSV files that contain critical historical data, alongside more recent datasets stored in Parquet or Iceberg. With Dremio, you can run a single query that spans these different formats, allowing you to access all relevant data in one go. This capability is crucial for businesses that need to maintain data continuity and leverage insights from both old and new datasets.

Dremio’s platform also optimizes performance through a variety of techniques and technologies. This means that whether you’re querying a small CSV file or a large Iceberg table, Dremio ensures that your queries run as efficiently as possible.

Future-Proofing Your Data Strategy

As your data continues to grow, Dremio’s lakehouse platform ensures that you’re always ready for the next step in your data journey. By supporting a wide range of data formats and providing advanced features like data reflections for query acceleration, Dremio enables your organization to scale its data operations without compromising performance or flexibility.

With Dremio, you don’t need to worry about outgrowing your data platform. As you move from CSV and JSON to Parquet, and eventually to Apache Iceberg, Dremio remains a constant, reliable tool that adapts to your needs. This future-proof approach allows you to focus on deriving value from your data, rather than getting bogged down by the complexities of managing it.

Moreover, Dremio’s open architecture ensures compatibility with a variety of data tools and frameworks, giving you the freedom to build a data ecosystem that works best for your organization. Whether you’re integrating with Apache Spark, leveraging machine learning models, or building real-time dashboards, Dremio serves as the backbone of your data operations.

Conclusion

The evolution of data storage—from the simplicity of CSV and JSON to the efficiency of Parquet and the advanced capabilities of Apache Iceberg—reflects the growing complexity and scale of modern data needs. As organizations progress through this journey, the Dremio Lakehouse Platform emerges as a crucial ally, offering seamless query capabilities across all these formats and ensuring that your data infrastructure remains flexible, scalable, and future-proof. Whether you're just starting with small datasets or managing a vast data lakehouse, Dremio enables you to unlock the full potential of your data, empowering you to derive insights and drive innovation at every stage of your data journey.

What should I do next?

• Download a Free Book About Apache Iceberg
• Schedule a Meeting to Better Understand Your Data Architectural Needs
• Get Hands-on with Dremio
• Learn How to Convert your JSON, Parquet and CSV data into Apache Iceberg