Optimize Data Lake Management with Databricks Delta Lake

The most successful companies in the world are making money by collecting, analyzing, and leveraging their data. Some are selling that data to other companies. Some are using data to drive innovation through R&D. Some are optimizing their business processes and cutting overhead costs. These companies are constantly exploring new ways to leverage their data.

Yet, the vast majority of business data still goes unused. It gets collected and stored but is rarely analyzed to aid decision-making. That’s because collecting the data is the easy part. Analysis requires modest investment, but taking action requires changing culture and building momentum in new ways.

New technology is facilitating the shift to data-driven culture and making using all that collected data easier. Databricks is leading the charge with their open-source storage layer, Delta Lake.

What is Databricks? 

Databricks is a large data analytics company founded by the creators of Apache Spark, an open-source data processing framework launched in 2009. Databricks provides cloud-based data engineering software for storing, processing, modifying, and analyzing large quantities of data and different numeric types. They are well known for their data warehouse, data lake, and data “lake house” solutions.

By incorporating machine learning functionality to process big data through a unified analytics platform, Databricks accelerates data processing, preparation, and sharing. Countless companies manage high volumes of data and integrate Databricks with cloud service providers like AWS, Azure, and Google Cloud, all of which provide native Databricks functionality in their services. 

What is Delta Lake? 

Delta Lake is an open-source storage layer that runs on top of an existing data lake. Delta Lake improves the data lake’s reliability, security, and performance and supports batch and streaming data processing, scalable metadata management, ACID transactions, and unified streaming. 

Enterprise organizations collect multiple data types from various sources constantly. This includes schema-based, schemaless, or streaming data. This data is typically stored in a data warehouse or lake using Delta tables, a Databricks-specific table construct that handles big data workloads scalably and efficiently. 

Before the era of big data in the early- and mid-2000s, data warehouses were one of the only options to store and manage business data. However, data warehouses are not robust enough to handle exceedingly large amounts of data (e.g., billions of rows), complex data types, or increased data flow velocity. They cannot natively process voice, audio, and IoT messages, facilitate exponential increases in data volume, or support streaming data for real-time analysis. 

A data lake is a central repository for storing multiple data types from various sources. While data lakes solve many of the limitations of data warehouses, they also introduce their own challenges, like poor query performance, lack of schema enforcement, and limited transactional support. 

Databricks Delta Lake addresses these concerns. Utilizing the Parquet-based, open-format storage layer, Delta Lake provides companies with better data visibility and accessibility. This, in turn, enables better data analysis and management. 

Benefits of Using Databricks Delta Lake 

Databricks Delta Lake marries the reliability and performance of data warehouses with the scalability, agility, and affordability of data lakes. 

Databricks Delta Lake is an open-format, secure, and reliable storage layer. Data storage capacity is available for structured (e.g., relational database tables with a fixed schema), semi-structured (e.g., JSON, XML, YAML), and unstructured data (e.g., text documents, IoT sensor data). Supported Databricks data types include numeric, date-time, binary, boolean, interval, and string. 

Databricks also supports many functions, including ACID transactions, scalable metadata handling, and optimized batch and streaming operations.  

Delta Lake 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 (compared to Parquet-base queries)
2. Improved data reliability
3. Broader data coverage across the system
4. Maintaining compliance
5. Constant data updates
6. Automated data engineering

Let’s explore some of the most important benefits of Delta Lake.

ACID Transactions

ACID stands for Atomicity, Consistency, Isolation, and Durability. All transactions made on Delta tables are ACID compliant by default. This is done by maintaining transaction logs called Delta Logs. 

Atomic transactions prevent incomplete files from entering the storage layer, while isolated transactions maintain consistency. After the transaction processes, data is committed for durability. ACID transactions ensure that data is always valid in or out of the source or destination.  

Scalable Metadata Handling

Delta Lake can scale out all metadata processing operations using computing engines like Apache Spark and Apache Hive. It does so with a transaction log file that tracks every change made to a Delta table and contains a record of every data operation (e.g., insert, update, delete) and operational metadata (e.g., timestamp, username). The transaction logs are stored separately from the data itself, which effectively outsources logging and provides a low-overhead way for data integration tools to harvest information, such as through change data capture (CDC). 

Delta Lake creates a checkpoint file after ten transactions are made with a fully transactional state and uses complex indexing mechanisms to maintain high performance and query efficiencies. A log file reader can process the checkpoint and transaction logs for more scalable metadata handling. This allows for the metadata to be efficiently processed for petabytes of data. 

Unified Batch and Streaming

Delta Lake manages data pipelines and incremental and streaming workloads by unifying batch and streaming data processing into one model. Because a Delta Lake table is both a batch table and a streaming source, it supports data ingestion for streaming and backfilling batch data. Data consumers, like data analysts and scientists, can easily use whichever data model is best for their tooling and workflows.

Schema Enforcement

Delta Lake will enforce a schema when writing data to the storage layer. This maintains columns and data types to achieve reliability and quality while preventing corruption. Additionally, Delta Lake allows users to modify schemas when explicitly enabled safely. 

One of the biggest frustrations for data engineers is schema drift, and upstream table schema changes. A single upstream schema change can cause major problems for data integration ecosystems without the right tooling and pipeline capabilities. Delta Lake provides a variety of mechanisms to ensure schema changes don’t break pipelines.

Time Travel

Time Travel is a powerful Delta Lake data lineage feature that allows users to reference previous dataset versions or roll back accidental data deletions or modifications. 

By running models on datasets for a specific date and time, teams can access old versions of data for temporal data management. Additionally, they can query Delta tables for specific timestamps. This is a useful feature for data analysts and scientists and anyone involved with data security and governance concerns. 

Using Delta Lake Tables in Databricks 

A Delta table is a file directory with two components: data objects and transaction log files. Data objects hold the table contents and are stored in Parquet format. A new transaction log is created anytime data is written to the Delta table. Each log file contains various actions that can be applied to previous table versions to obtain a new version.  

Databricks Delta tables can record version changes or modifications to a class of tables in a data lake. Databricks Delta tables enable ACID transactions, time travel features, and other features described above. 

Delta Lake tables convert stale data files and transaction logs from Parquet to Delta format, which reduces custom coding requirements. Advanced features provide visibility into project history over time and allow users to update, delete, and merge operations to avoid duplicative efforts. 

In other words, Delta Lake alleviates many of the challenges data engineers have faced for decades in dealing with primitive data types and legacy relational databases. 

Challenges of Using Databricks 

Databricks Delta Lake is a powerful technology but comes with a few challenges. Given its advanced capabilities, there can be a considerable learning curve, though IaaS providers are doing their best to provide seamless Databricks integrations in their services. Delta Lake can also be challenging regarding cost, performance, compatibility, and infrastructure management (depending on who is hosting and how).

On the data consumption side, making data easily accessible to customers can be difficult for organizations that use Databricks as their primary data transformation and management tool. Data consumers should know where to find the data they need and how to access it with the correct permissions. We recommend using a data catalog and a data marketplace to ease the friction of sharing data internally.

Understanding what data exists in the marketplace and how to leverage that information for the best business operations can be complicated and overwhelming for consumers. Purchasing processes vary from internal chargeback systems to traditional online transactions, adding additional friction. Companies typically turn to a sophisticated data-sharing tool like Revelate to remedy these challenges. 

Databricks Data Sharing Made Simple with Revelate 

Revelate enhances data collection and analysis and enables teams to leverage data for strategic decisions better. Other benefits include making it easier to commercialize data and expanding the accessibility of data sharing by simplifying complexities through a secure environment. 

Organizations can provide a better data-sharing experience for customers while enforcing related policies and entitlements. Pre-configured access and usage rights help data providers avoid decoupling data requests from manually servicing data. 

Revelate integrates with any Databricks account to facilitate a secure and optimized data exchange in an organization’s Delta Lake. It also provides a marketplace where Databricks users can sell and share data. 

Teams using Databricks with Revelate can more easily make data publicly available publicly, internally or to partner networks, and data consumers can go through a data purchasing process that resembles traditional e-Commerce transactions they’re familiar with. 

Fitting into existing data ecosystems, Revlate can handle first-party and third-party datasets. Teams can securely manage PII data and other sensitive information and easily share complete datasets or create granular data products. 

The benefits of optimized data sharing include enhanced business innovation and product development, increased revenue through data monetization, better strategies based on insights, and a more comprehensive understanding of business processes. 

Databricks Delta Lake Frequently Asked Questions 

Are DataBricks and Cloudera direct competitors? 

While DataBricks and Cloudera provide services and solutions for big data analytics and processing, they operate in different market areas. There is some overlap between the services offered by DataBricks and Cloudera, but they are not considered direct competitors. 

DataBricks is focused on providing a cloud-based data science and engineering platform, while Cloudera is more focused on enterprise-grade big data processing and management. 

DataBricks was founded by the original creators of Apache Spark and has since expanded to offer a range of data-related services, including machine learning and analytics. DataBricks is known for its ease of use and scalability and is popular among data scientists and developers.

Conversely, Cloudera enables organizations to store, process, and analyze large amounts of data. Cloudera was one of the first companies to commercialize Apache Hadoop and has expanded to include other open-source technologies such as Apache Kafka and Apache Spark.

Is Azure Databricks an ETL tool? 

While not technically an ETL (extract, transform, load) tool, Azure Databricks can be used as part of an ETL pipeline. 

Azure Databricks is a cloud-based data analytics platform providing a collaborative workspace for data engineers and scientists to build, train, and deploy machine learning models at scale. 

While Azure Databricks does not have a built-in ETL tool, it does provide various tools and APIs that can be used to perform ETL tasks. Databricks also provides data wrangling and transformation tools, like Spark SQL, DataFrames, and Delta Lake.

Should I go for Databricks or PySpark?  

PySpark is an open-source distributed computing framework built on top of Apache Spark. PySpark provides a Python API that allows data scientists and developers to write Spark applications using Python code. PySpark can process large amounts of data in a distributed fashion to improve performance and scalability.

PySpark can run on various platforms, including on-premises and in the cloud, and has a large and active community of users and contributors.

Databricks is also built on top of Apache Spark and offers an intuitive user interface, powerful tools for data transformation, and integrations with cloud service providers like Azure, AWS, and more. This tool provides a fully-managed service for managing many of the operational and administrative tasks associated with running Spark applications and offers robust solutions and integrations for data transformation and analysis.

PySpark is a more simple and more cost-effective option for running Spark applications, while DataBricks is more robust and user-friendly and better for scaling to manage large quantities of data.