What is a .parquet file?

A .parquet file is a data format called DELTA. Delta Lake Table

How do I open a .parquet file without installing software?

Upload your .parquet file to OpenAnyFile. Our browser-based tool instantly detects and displays it — no downloads needed.

What program creates .parquet files?

.parquet files are typically created by Databricks.

Can I convert .parquet to another format?

Yes. OpenAnyFile converts .parquet to PARQUET, CSV.

Is it safe to open .parquet files online?

Yes. OpenAnyFile processes files in a secure, isolated environment. Files are automatically deleted and all transfers use HTTPS.

Can I open .parquet on my phone?

Yes. OpenAnyFile works on all mobile browsers including iPhone and Android.

Open DELTA Files Online Free - View & Convert DELTA

Quick context: The DELTA file format, often referred to as Delta Lake, is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to Apache Spark and large data workloads. Developed by Databricks, it provides reliability, security, and performance on data lakes, enabling a single data pipeline for streaming and batch processing. It acts as an abstraction layer over data stored in object storage (like S3, ADLS, GCS) typically in Parquet format.

Technical Structure

A DELTA Lake table is not a single file but a collection of files and a transaction log. The core components are:

Data Files: The actual data is stored in Parquet format by default. Delta Lake leverages Parquet's columnar storage for efficient querying and compression. When data is updated or appended, new Parquet files are written rather than modifying existing ones,
Transaction Log: This is the defining feature of Delta Lake. Stored as a series of JSON files (and eventually compacted into Parquet files for efficiency), the transaction log records every operation performed on the table (e.g., additions, deletions, updates, schema changes). It ensures ACID properties by maintaining the order of operations and enabling features like time travel. Each commit creates a new JSON file, e.g., 00000000000000000000.json, 00000000000000000001.json.
_delta_log directory: This subdirectory contains the transaction log files and checkpoint files, which are compacted versions of the transaction log history, speeding up queries.

This architecture allows for versioning, schema enforcement, and atomicity, ensuring data integrity even with concurrent operations.

How to Open DELTA Files

Directly "opening" a DELTA file in the traditional sense, like a document or image, isn't applicable because DELTA represents a table, not a singular viewable file. To access or interact with data in a Delta Lake table:

Apache Spark: This is the primary method. Spark provides native integration with Delta Lake. You can read a Delta table using spark.read.format("delta").load("/path/to/delta/table").
Databricks Runtime: As the originators, Databricks fully supports Delta Lake, and it's seamlessly integrated into their platform.
Delta Lake API/Connectors: Libraries exist for various programming languages (e.g., Python, Scala, Java, Rust) to interact with the Delta Lake transaction log and data files.
OpenAnyFile.app: While you can't view the entire transactional table directly in a web browser, tools like OpenAnyFile.app can help individual components. You can [open DELTA files](https://openanyfile.app/delta-file) if you're referring to the individual Parquet or JSON log files that compose the Delta table. If you're looking for how to open DELTA data, you'll generally need a data processing environment. For broader access to various [Data files](https://openanyfile.app/data-file-types), explore our [all supported formats](https://openanyfile.app/formats) list.

Compatibility

Delta Lake boasts strong compatibility within the data ecosystem:

Apache Spark: Deeply integrated, it is the de-facto processing engine for Delta Lake.
Cloud Object Storage: Works seamlessly with AWS S3, Azure Data Lake Store Gen2, Google Cloud Storage, and HDFS, as it stores its underlying data and transaction logs on these systems.
Third-party Tools: Increasingly, business intelligence (BI) tools and data warehousing solutions are adding connectors or native support for Delta Lake tables.
Open Standard: As an open-source project, Delta Lake aims for broad interoperability, although some advanced features might be tied to Databricks Runtime.
File Formats: Primarily interacts with Parquet files for data storage, and JSON for transaction logs. Other formats like [KDL format](https://openanyfile.app/format/kdl) or [LAS format](https://openanyfile.app/format/las) are distinct and not directly part of the Delta Lake structure.

Common Problems and Solutions

Users of DELTA Lake tables may encounter specific challenges:

Performance Issues (Small Files): Frequent small appends can lead to many small Parquet files, degrading query performance.
Solution: Use OPTIMIZE commands with ZORDER to compact files and collocate related data.
Schema Evolution Conflicts: While Delta Lake supports schema evolution, incompatible changes can lead to errors.
Solution: Utilize mergeSchema option or explicitly evolve schema carefully. Reviewing the transaction log (_delta_log) can help diagnose issues.
Time Travel Complexity: Understanding specific versions or reverting changes can be complex without clear operational practices.
Solution: Document schema changes and major operations. Use version numbers or timestamps effectively with time travel queries.
Storage Costs: Versioning and immutable data files can lead to increased storage usage over time.
Solution: Implement VACUUM commands to remove old, unreferenced data files after a defined retention period.

Alternatives

While Delta Lake offers unique advantages, other solutions exist for managing large-scale data:

Apache Iceberg: Another open table format providing ACID properties on data lakes, originating from Netflix. It offers similar features like schema evolution and time travel with different underlying architectural choices.
Apache Hudi: Developed by Uber, Hudi (Hadoop Upserts Deletes and Incrementals) also provides transactional capabilities on data lakes. It supports record-level updates and deletes, which can be beneficial for specific use cases.
Traditional Data Warehouses: Solutions like Snowflake, Google BigQuery, or Amazon Redshift offer fully managed ACID-compliant data storage and querying, often at a higher cost or with vendor lock-in. These are robust, but typically less flexible for raw data lake scenarios.
Pure Parquet + Hive Metastore: Before open table formats, many data lakes used Parquet files combined with a Hive Metastore for schema management. This lacks ACID transactions and advanced features like time travel.

Consider your specific requirements for data integrity, performance, cost, and ecosystem integration when choosing a solution.

FAQ

Q: Can I edit a DELTA file directly?

A: No. Delta Lake tables are managed by a transaction log. Edits are performed via computational engines like Apache Spark, which write new data files and update the log.

Q: How do I [convert DELTA files](https://openanyfile.app/convert/delta) to other formats?

A: You typically load the Delta Lake table using Spark (or a similar engine) and then write the data out in your desired format, such as [DELTA to PARQUET](https://openanyfile.app/convert/delta-to-parquet) or [DELTA to CSV](https://openanyfile.app/convert/delta-to-csv).

Q: Is Delta Lake really open source?

A: Yes, the core Delta Lake project is open source under the Apache 2.0 License.

Q: What is "time travel" in Delta Lake?

A: Time travel allows you to query an older version of your Delta Lake table by specifying a timestamp or version number. This is enabled by the immutable nature of the data files and the detailed transaction log.