Delta Lake Databricks A Deep Dive

Delta Lake Databricks: A Deep Dive

Revelate

Table Of Contents

In recent years, data lakes are preferred over data warehousing. In fact, a study revealed that the data lakes market reached $3.74 billion in 2020 and is expected to reach $17.60 billion by 2026.

One data storage system is Delta Lake by Databricks, a platform providing open-source innovations that drive more efficient transactions, especially with data management and security. Combining it with Databricks’ lakehouse platform elevates your company’s data storage capacity.

This guide talks about Delta Lake by Databricks, its features, and its benefits. It also goes over the various functionalities of the platform to help you determine how this service can benefit your business.

What is Delta Lake Databricks?

Delta Lake Databricks is an open source advanced storage layer in the Databricks Lakehouse Platform. It establishes an efficient foundation where your data and tables can be stored in Databricks. You can extend your Parquet data files through Delta Lake for scalable metadata handling and ACID transactions by combining them with file-based transaction logs.

Delta Sharing Impact on Databricks Delta Lake

Because of Delta Lake’s efficiency, Databricks has allowed incremental scale processing by using only a single data copy for streaming and batch operations. Delta Lake is also compatible with Apache Spark APIs, Pandas, Rust, and other popular systems.

With Databricks Delta Lake, data shares can be done without the need to create a compute pattern first. As a result, it becomes smoother and easier for organizations to share data, especially across a diverse client base.

Finally, Delta Sharing has allowed organizations to share datasets as large as a terabyte through cloud storage systems. A few of the systems that are compatible with Delta Lakes include ADLS, S3, and GCS.

Benefits of Delta Lake Databricks

Delta Lake Databricks accounts for around 75% of the data scanned into the Databricks platform. As long as you’re using default settings when you’re saving data to the lakehouse, you can enjoy the following benefits of Delta Lake Databricks:

  1. Faster query performance
  2. Improved data reliability
  3. Broader data coverage across the system
  4. Maintaining compliance
  5. Constant data updates
  6. Automated data engineering

Let’s go over these benefits in more detail.

Query Performance

Establishing faster query performance is crucial, especially when a company needs access to a specific group of data in a short amount of time. With Databricks Delta Lake, it’s faster to generate the same query compared to a regular parquet.

Databricks Delta Lake does not require an extensive and costly LIST operation, which is the standard procedure in most Parquet readers. Instead, Databricks utilizes a transaction log, which acts as a manifest.

Delta transaction logs centralize essential statistics while also keeping track of Parquet file names. The statistics include minimum and maximum values you can find in Parque file footers. Through the statistics, Delta Lake does not have to ingest files after determining they don’t match query predicates.

As mentioned, Delta Lake Databricks is an open-source project. This means a growing community is built around it, with more query engines supporting the project. If you’re one of the individuals or organizations using the query engines, you can start utilizing Delta Lake and take advantage of its benefits.

Because of improved query performance, the following techniques are also assisted:

  • Skipping⁠—reading only relevant data portions to maintain file statistics
  • Indexing⁠—ensuring properly arranged queried data by maintaining indexes on Delta tables
  • Caching⁠—improving run times for run queries because of automatic caching of highly accessible data
  • Compression⁠—efficient Parquet file management, consuming less memory

Data Reliability

You can expect more reliable data storage with Delta Lake because it involves complementing big data on-site rather than in the cloud. Delta Lake Databricks was also designed to allow blob storage accommodation to ensure data quality and consistency.

Even when ETL jobs fail because of a Delta Lake table before their completion, you need not worry about query corruption. You can also expect all SQL queries to remain in a consistent state in the Data Lake table.

Thanks to the consistency, data engineers are able to allow ETL troubleshooting when it fails or needs a rerun. Databricks can accomplish ETL jobs and troubleshooting without alerting users, returning to their previous state, or removing partially written files.

Databricks Delta Lake also allows traditional data partitioning but at a faster pace. Before, a failed partition meant repeating everything and creating a new one. This affects the system’s query performance.

But with Databricks Delta Lake, it eliminates repeated partitions in the event of a failure. This results in more efficient data storage and performance because fewer files are scanned.

Another reason why Delta Lake Databricks offers enhanced data reliability is because of fewer constraints in the partitions. This system enables data engineers to choose between two things:

  • Enforcing their preferred schema
  • Allowing the evolution of an old schema

A change in schema incompatibility allows Databricks Delta Lake to prevent data corruption in columns with incompatible types. Databricks Delta Lake can also include “not null” column constraints. This is one thing you cannot accomplish with a standard Parquet table.

This feature prevents your data from overloading with null values, which also ensures your system’s processes will not experience a downstream.

Finally, Delta Lake Databricks promotes data reliability with support from the MERGE statement. The MERGE statement can allow data pipeline configuration and ensure a new record insertion, ignoring currently present records in the Delta Table.

Delta Lake Databricks also allows snapshot isolation. This ensures the simultaneous writing of datasets by multiple writers without causing job interference. With the data checkpoints, they ensure information is read and delivered only once.

System Complexity

Delta Lake Databricks ensures broader coverage use across the entire data ecosystem. Aside from ensuring top security and performance, Delta Lake also promotes simplicity in complex systems. It creates a unified framework for the following purposes:

  1. Streamlining workloads
  2. Enhancing efficiency in data transformation pipelines and downstream activities
  3. Governing access controls for improved security and data confidentiality

Delta Lake Databricks also ensures better engine performance and support for the broader ecosystem. Additionally, it has been tested to have a capacity of more than 3PB of data ingestion.

Delta Lake simplifies system complexities by accomplishing the following:

  • Ingesting data quicker to query results
  • Promoting more basic architecture
  • Quickly responding to sudden changes by ensuring flexible architecture on data analytics
  • Inferring data schemas for better input

Ensures Compliance

Data storage should comply with CCPA and GDPR. These regulations require data purging exclusive to a particular customer when a request is made.

The following table breaks down some of the differences between CCPA and GDPR regulations for data storage.

CCPA GPDR
Applies to profit entities that process the personal data of residents of California. Organizations must also meet additional criteria, such as having more than 50,000 data records, having a profit of more than $24 million, and earning more than half their revenue from personal data sales. Applies to any entity that processes the personal data of citizens of the EU. This applies to organizations that aren’t located in the European Union.
Involves opt-in consent Involves opt-out consent
Enforcement began in January 2020 Enforcement began in May 2018
Individuals have the right to access information as well as to request information. Individuals have the right to access their information.
Individuals have the right to opt-out of information collection Individuals don’t have the right to opt-out, but they can get around this using other protections under the GDPR.
Only applies to data collected from the consumer Applies to all data involving the consumer

With a standard Parquet data lake, data updating and purging are compute-intensive. This means that each requested data should be identified, filtered, ingested, and rewritten as new files. The original file should also be deleted, and all these should be accomplished while ensuring no data is disrupted or corrupted.

On the contrary, Delta Lake Databricks enables update and delete actions without the hassle of rewriting all the data. This allows easier manipulation of data and better regulatory compliance. As a result, businesses can avoid the large fines and penalties that come with poor compliance.

Constant Data Updates

Standard Parquet Data lakes allow consistent data refresh, but not every minute. Sometimes, technical challenges link data updates into a certain aggregation. It’s different from Delta Lake Databricks, which allows constant streaming and data ingestion from the beginning.

Because Delta Lake is accompanied by structured steaming, companies can get built-in automatic checkpoints during data transformation. The result is that data is constantly updated and monitored for any discrepancies.

Once again, this leads back into compliance. With constant data updates, companies ensure that they’re staying compliant with current regulations.

Automated Data Engineering

Everyone knows that automation means faster and more efficient data management. Databricks’ automated data engineering has simplified Delta live tables, allowing easier control and building of data pipelines.

Databricks Delta helps engineering teams to manage ETL processes using could-scale production and declarative pipeline development.

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Key Features of Databricks Delta Lake

Key Features of Databricks Delta Lake

There’s a reason why companies rely on Delta Lake from Databricks. It provides a wide range of key features you won’t find in traditional data warehousing. This cost-effective and scalable lakehouse foundation offers an innovative way for batch and streaming operations, ensuring improved data security and efficiency.

The table below summarizes the most essential key features your company can enjoy with Databricks Delta Lake:

Key Feature Purpose 
ACID Transactions Ensures efficiency with automatically written transactions
Scalable Metadata Handling Enables handling of large-sized data without lagging or causing delays
Unified Batch and Streaming Eradicates the need for separate data architectures
Schema Enforcement Prevents badly written schema
Upserts and Deletes Allows easy merging of data

ACID Transactions

ACID stands for:

  • Atomocity – complete success or failure of all transactions
  • Consistency – guarantees simultaneous operations of data
  • Isolation – quickly identifies possible conflicts in simultaneous data operations
  • Durability – ensures permanent committed changes

Atomicity ensures Delta Lake Databricks is an all-or-nothing system. Even when it consists of various steps, all steps serve as a single unit or operation.

Next, consistency requires constant transaction updates according to the system’s constraints and rules. Isolation, on the other hand, allows concurrent and parallel data access. This enables transaction modifications to be done while not affecting another operation.

Parallel transitions in isolation can also permit sequential operation with Databricks Delta Lake. With durability, database and transaction modifications are saved in disks, which are permanent storages preventing sudden loss or damage.

The following are the benefits of ACID transactions:

  1. Guarantees data integrity, ensuring all information is valid, accurate, and within the system constraints.
  2. Simplified operational logic, reducing complicated update operations
  3. Ensures storage reliability

Scalable Metadata Handling

Databricks Delta Lake makes handling metadata much simpler, in addition to the many other benefits it provides to organizations. Through the Describe Detail syntax, companies can freely store metadata in any way they prefer. Scalable metadata handling refers to new data onboarding into a particular governed data landscape.

From there, it distributes processing power in order to handle the metadata for numerous files without slowing down processes.

The main advantage of scalable metadata handling is that it contributes to effective data governance, discovery, and utilization. This increases your company’s productivity with information storage because it ensures the freshness and quality of your data for broader consumption.

Other benefits of scalable metadata handling include:

  • Reduced cost because of fewer risks of redundancy
  • Better data quality because of automation
  • Faster data analysis and project delivery
  • Less risk of data retrieval issues

Unified Batch & Streaming

Databricks Delta Lake doesn’t require separate architectures for data stream monitoring. This helps in overcoming the limitations of batch systems and streaming. Unified batch and streaming lead to operational improvement and boosted productivity because users can maintain fewer environments.

A unified system also speeds up pipeline streaming and application. This key feature also enables users to take advantage of Databricks’ foundational components, especially with its lakehouse platform. Delta Lake allows raw data optimization, leaving you with integrated and fine-grained data governance for all your assets.

Unified batch and streaming also promote better native support, which is relevant to Delta sharing. Delta sharing refers to the industry’s open protocol for promoting better and simpler data sharing.

Schema Enforcement

Schema enforcement or validation ensures data quality in Delta Lake by rejecting table data that do not match its schema. It serves as a front desk manager ensuring only those with reservations enter a restaurant.

Schema enforcement ensures all data in table columns are expected and are on the list. Databricks Data Lake provides schema enforcement on write. This means that new input to table columns should be checked for compatibility. For incompatible schema, Delta Lake ensures transaction cancellation.

Schema enforcement provides an excellent tool for gatekeeping data, ensuring that only fully transformed and clean information is ready for consumption and production. The following are the most beneficial tables, thanks to schema validation:

  • Visualization tools
  • Machine learning algorithms
  • Data analytics
  • BI dashboards

Upserts and Deletes

Delta Lake Databricks is one of the few systems that encourage these operations. Upsert refers to Databricks’ efficient merging of data without delays. Databricks Delta Lake also allows data update and deletion while a predicate exists on the Delta table.

Additionally, deleting Delta Lake tables is straightforward. There are several times when upserting and deleting are beneficial for Databricks users.

For instance, these features are great for achieving compliance with General Data Protection Regulation. Together with data erasure, this feature allows organizations to remove specific users’ information.

Additionally, upserting and deleting are also efficient for changing data capture in a traditional database. This is especially useful when data engineers need to build pipelines for data consolidation. Upserts and deletes also promote deduplication and sessionization.

Databricks Delta Functionalities

Databricks Delta Functionalities

Databricks Delta Lake allows quick turnaround time for data handling and storage. With the following key functionalities, Databricks provide data engineers with better solutions to ensure system reliability and better performance.

Here’s a closer look at some of the functionalities of the platform and how they can benefit your business.

Simple Transition From Spark to Delta

Databricks Delta also allows a quick and simple switch from Parquet to Data. The transition occurs with less custom coding because Delta tables already have metadata and allow other functions, like upserts and delete.

Fine-grained Updates

Databricks data also allows fine-tuning and updating of records without overwriting the entire file, unlike standard Parquet. This means you can edit table records through a partition level. Databricks Delta also allows deduplication, simplifying data updates, and merging them.

In this way, companies can easily track and handle changes made.

Fine-Grained Deletes

Aside from editing and updating data records on a partition level, users can also fine-grain deletes without overwriting the entire file. Databricks Delta allows users to delete selective rows based on a predicate.

After a successful delete operation, Delta Lake does not remove your old data files. They remain on your disk and are labeled as “tombstoned” instead. Even though they are not part of your active Delta tables, you can still check them out when you need to time travel.

But if you wish to delete files permanently, you can choose the vacuum command.

Query Your Data Lake Directly

Databricks Delta also allows direct query, enabling consistent reading during updates, appends, and deletes. This improves the overall productivity and efficiency of your data storage.

Time Travel

Time travel is a unique functionality of Databricks Delta that allows quick rollback of users for bad writes. This also promotes better temporal data management, allowing data scientists to refer to previous data versions.

Time travel also allows companies to query Delta tables according to certain timestamps. It also permits auditing, reproducing reports and experiments, and rolling back accidental deletes.

Databricks Delta Sharing Goals

Databricks Delta Sharing is designed to meet several goals, such as sharing live data without having to copy it or supporting a larger range of clientele. The table below outlines a bit more detail on these goals.

Goal Explanation
Share live data directly without copying it This makes it easy for individuals to share information in real-time. Since Delta Sharing is compatible with lakehouse and cloud data lake systems, you can securely share information in Parquet or Delta Lake formats without worrying about third parties gaining unauthorized access.
Support a wide range of clients Delta Lake is compatible with a number of different formats, meaning that consumers can use the tools they like without having to move to a new platform. Additionally, since the platform is on Parquet, it allows users to implement a connector that lets them use any other tools.
Strong security, auditing, and governance Delta Sharing allows you to grant access as well as track and audit access to shared data. All this is done from a single origin point, which helps support compliance and enables businesses to meet privacy regulations.
Scale to massive datasets Delta Sharing uses a cloud system to safely, reliably, and affordably share datasets of up to a terabyte in size.

Who Should Use Databricks Delta?

Who Should Use Databricks Delta

Databricks Delta is most useful for organizations that need extra data solutions for handling information within their data lakes. Additionally, organizations that fall into the below categories can also benefit from Databricks Delta:

  1. Companies that are utilizing Databricks components and need an additional data pipeline solution for building their data lake
  2. Those that want a simpler data pipeline architecture with better efficiency, productivity, and performance
  3. Organizations in need of better data processing capability because of large datasets
  4. Companies that want better metadata management, upserts, backups, and data consistency

On the other hand, Databricks Data is only recommended for some organizations. A few companies that may not be able to gain many benefits from Databricks Delta include:

  • Those that prioritize gaining access to the data lake over maintaining data consistency
  • Companies with small datasets that can work with traditional database solutions
  • Those that need constant data extraction
  • Those that use GCP as the cloud provider

How Databricks works with Revelate

Revelate is an efficient platform that allows data distribution, regardless of the time and place. It ensures that Databricks partners can access licenses and privileges companies need for efficient data security and transactions in an open yet protected environment.

On top of that, Revelate makes sure that it’s easy for Databricks’ customers to share data. Revelate can be connected to any account to make it easier to share data in a client’s Data Lake.

Revelate also offers a Data Marketplace. This makes it easy for businesses to package, sell, and monetize their data. Alternatively, they can share the data more easily with other third parties.

Finally, the data Data Marketplace offered through Revelate allows technical employees to stop focusing on fulfillment and to use their time towards other objectives. This can help improve data engineering pipelines or to put high-value employees’ time toward better uses.

Conclusion

Utilizing improved data engineering solutions is crucial for organizations. Doing this can serve as a primary driver to have a better quality of information and more secure data sharing. Data engineers are now presented with better data pipelines, improving their overall efficiency.

Revelate offers better data-driven solutions for your company, whether it’s through data monetization, organization, or improving data-driven decisions. Schedule a demo with Revelate to learn more about the platform and how it can support an enhanced organization.

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started