In this modern era, most companies use their key resources to the maximum advantage in Big Data analytics applications namely Datalake and Data Warehouse. This helps to store data and brings insight into an in-depth understanding of people and places to deliver objectives for better decision-making. A Data lake provides a place, room, or container where something is stored or deposited in a scalable form. However, businesses have a close encircling with data warehouses for analyzing structured or semi-structured data. However, there is a remarkable unified analytics platform i.e., Databricks which combines the benefits of both Datalake and Data Warehouse by providing Data House Architecture (DHA).
This DHA facilitates Deltalake to hold raw and intermediate data in the Delta table. This can be enabled while performing Export, Transform, and Load (ETL) and other Data processing Tasks. Besides, the Databricks Delta table is designed to reduce data transmission time and send updated data. This process helps to facilitate the data pipeline most efficiently.
This blog demonstrates how Delta Live Table helps you to develop scalable, reliable data pipelines which helps data quality standards in Lakehouse architecture.
Delta Live Table – Meaning
It is the first ELT framework that can be used for building reliable data and maintainable, testable data processing pipelines on Delta Lake. It simplifies ETL (Export, Transform, and Load) development and helps with automatic data testing, monitoring deep visibility, and recovery of pipeline operations.
Doug Henschen the principal analyst at Constellation Research said, “DLT are an upgrade for the multi-cloud Databricks platform that supports the authoring, management, and scheduling of pipelines in a more automated and less code-intensive way”.
DLT uses a simple declarative approach to build reliable data pipelines. It helps reduce the time the data engineers and data scientists take by automatically managing infrastructure.
DLT aimed to ease the struggle of writing codes for bigger companies and maintaining, and running data pipelines by way of automating many of the codes to keep the data pipeline in smooth flow.
DLT is supported by Python and SQL (till date 12-09-2022)
Features of Databricks Delta Live Table
The key features of Delta Live Table have been discussed below,
- Automated Data Pipeline
To simplify data source, transformation logic, and destination state of data this automated data pipeline defines an end-to-end data pipeline instead of traditionally or manually combining complicated data processing jobs.
DLT automatically maintains all the data across the pipeline and helps to reuse the ETL pipeline for independent Data Management.
- Automatic Testing
To prevent bad data from flowing into the tables, automatic testing validates the data and makes integrity checks, and it avoids Data Quality Errors.
It also allows the Data Engineers and Data Scientists to monitor Data Quality trends to derive insight into required changes in the performance of data
- Automatic Error-Handling
To reduce downtime in the data pipeline an automatic error-handling system has been used. By doing this DLT gains deeper visibility into pipeline operations with tools that will visually track the operational statistics and data lineage.
Some of the additional features of Delta Live Table
- It supports Selective Table Refresh.
- It does not support viewing the pipeline from another cluster or SQL endpoint.
- It can perform maintenance tasks on tables every 24 hours. This can be done by running the OPTIMIZE and VACCUM commands. By using these commands, we can improve performance and reduce costs by removing the traditional version of tables.
- Often each table can be defined once. If we want to combine multiple inputs to create a table ‘UNION’ can be used.
- It will retain all the history for seven days as a custom-defining retention period to query the snapshot of tables.
Concepts and Terminologies used in the process of Implementation of Delta Live Table
Before implementing Delta Live Table, we need to know the meaning of different concepts and terminologies used in DLT i.e.,
| Terminologies | Meaning |
| Pipeline | We cannot run a fixed capacity cluster 24/7 or 365 round the clock to support these updates. |
| Latency | While ingesting new data, it is visible in the destination (e.g., silver layer) within 5 seconds. |
| Cluster usage | We cannot run a fixed capacity cluster for 24/7 or 365 round the clock to support these updates. |
| Cost per DBU | DLT Core – $0.30 DLT Pro – $0.38 DLT Advanced – $0.54 |
| Accuracy | Speaking about how much should Data Engineer or Data Scientist account for later arriving data in their real-time sources. |
| Streaming | Here the data sets are treated as unbounded |
| Incremental | It is an updated pattern in which minimal charges are made to the destination data. |
| Continuous | Refers to a pipeline i.e., always runs until it is stopped at an arbitrary time. |
| Triggered | Refers to a pipeline i.e., it will not run until the start button is pressed. |
Implementation of DLT
The following simple steps can be used to implement DLT in the organization.
- Design Lakehouse Zones
Primarily, we need to design all the layers of the lakehouse platform i.e., Bronze Layer, Silver Layer, and Gold Layer.
The Bronze Layer consists of raw data which is received for audit purposes to track back to the data sources. This bronze zone consists of incremental live tables. We ingested them using the Auto-loader feature using the cloud file function. We ingested them using the Auto Loader feature using the cloud file’s function. In DLT, we can view data like a temporary view in SQL, but a view allows us to break a complicated query into smaller or easier ways to understand the queries. In DLT (Under this Bronze Layer), tables are like traditional materialized views. The DLT runtime automatically creates tables in the Delta format and ensures updates based on the latest result of the query that creates tables.
From the customers’ point of view, they can see the tables, and view Delta Lakehouse from the point of standard Delta tables, but they are being updated and managed by the DLT engine.
Next to the Bronze Layer, the quality, diverse, and accessible datasets can be found in the Silver Layer. This zone filters data and cleans the data from the Bronze Layer. This Layer handles the missing data and cleans fields in a standardized way. For easy query, this zone converts the nested objects into flat structures. It can also rename the columns into friendly names for better understanding. In this Layer, each customer can have confidence in the quality of the data they are using. Finally, we can discuss the Gold Layer. When the data reaches the Gold Zone, it aggregates the data based on its dimensions and facts and it creates a business-specific model. It provides business-friendly names and creates views for business users. The customers also can access these views. In this layer, DLT allows us to choose whether each dataset in a pipeline is incremental or complete. This Layer makes it easy to scale the pipeline involving the combinations of bronze and silver real-time data with gold aggregation layers.
- Implement Quality Rules
To implement quality rules, DLT provides a simple and extremely easy mechanism to clean the data when the conditions do not match. DLT also provides the EXPECT clause and instructions on what to do. If we find any invalid record, we may choose some of the actions like dropping the invalid record or retaining the invalid record or we can fail the pipeline.
- Deploy, Test, and monitor.
Once we deploy Delta Live pipelines, we must perform the test to monitor everything is working as expected. DLT provides a unique monitoring capability.
DLT’s functionality in a simple matrix.
To understand the DLT functionalities straightforwardly, the following matrix has been drawn i.e.,
| Read From | Write To | Continuous Mode | Triggered Mode |
| Complete | Incremental | Not Possible | Not Possible |
| Incremental | Complete | Reprocess on a predefined interval | Reprocess the materialized stream result. |
DLT Demo
Step 1: Login to your Databricks, if you do not have an account, click the link below to create a new account. https://accounts.cloud.databricks.com/registration.html
Step 2: Click Create à Notebook
Step 3: Give a name to your Notebook, select the default language as Python or SQL, and select the cluster finally press create.
Step 4: On the Notebook develop the DLT program in the language that you have chosen above.
Step 5: Now create a pipeline by clicking Create à Pipeline
Step 6: Now enter the Pipeline name, select the notebook libraries, enter the Target name, and storage location, and select triggered or continuous in the pipeline mode, and choose workers on cluster based on your need.
Step 7: To run the DLT pipeline click the Start button.
Step 8: The UI will show you the logs, data quality, flow, etc.
Step 9: DLT will create tables along with the flow within them, once the pipeline runs successfully you can see the flow diagram of the tables.
Conclusion
After going through this blog, you have stepped through your first DLT pipeline and learned some of the key concepts. For more information see our DLT documentation and watch a DEMO.
For More Details, Diggibyte Technologies Pvt Ltd has all the experts you need. Contact us Today to embed intelligence into your organization.
Author: Xavier Don Bosco
References
https://accounts.cloud.databricks.com/registration.html
https://docs.microsoft.com/en-us/azure/databricks/workflows/delta-live-tables/
https://www.databricks.com/discover/pages/getting-started-with-delta-live-tables
https://rajanieshkaushikk.com/2022/06/24/why-the-databricks-delta-live-tables-are-the-next-big-thing/