A Data Lake is a Storage Repository that holds a large amount of Unstructured, Semi-Structured, and Unstructured data in its natural format. On top of Data Lake, Delta Lake is an Open-Source Storage Layer built on Spark. Its key functions ensure Data Integrity with ACID Transactions while also allowing reading and writing from/to the same directory/table, bringing reliability to massive Data Lakes. ACID stands for Atomicity, Consistency, Isolation, and Durability.
Even if Spark Operations fail, Delta Lake ensures that data is never lost during ETL and other Data Processing Operations. Delta Lake has evolved into more than a staging place, but it is not a real Data Lake. It’s a “Delta Lake”, as its name implies. It’s still mostly used to ensure that all “Deltas” from Spark tasks are never misplaced. It aids in ensuring that the final data loaded into a Data Warehouse is accurate.
In this in-depth article, you will learn about Delta Lakes for fulfilling your Data Storage and ETL processes efficiently.
Table of Contents
What is Delta Lake?
Delta Lake is an Open-Source Data Storage Layer that ensures Data Lakes’ dependability. It unifies ACID Transactions, Scalable Metadata Management, and Batch and Streaming Data Processing. The Delta Lake design sits atop your existing Data Lake and works in tandem with Apache Spark APIs.
Key Features of Delta Lake
- Scalable Metadata Handling: Delta Lakes are capable of handling even petabytes of data with ease. It stores metadata just as it stores data and users can access it using Describe Detail feature.
- Schema Enforcement: Delta Lakes are widely used by companies because it enforces the schema. It reads the Schema as a part of the metadata and looks at every column, data type, etc.
- Unified Batch and Streaming: Delta Lakes provides a single architecture for reading stream data and batch data as well.
- Upserts and Deletes: Delta allows you to make upserts easily. These upserts or merges are similar to SQL Merges into the Delta table. It allows you to merge data from another data frame into your table and apply updates, inserts, and deletes.
For further information on Delta Lakes, check out the official website here.
Why Delta Lake?
Let’s look at the key advantages of adopting a Delta Lake in your business’ Data Stack.
1) Problem with Today’s Data Architectures
Big Data Architecture is difficult to create, manage, and maintain at the moment. In most modern Data Architectures, at least three different types of systems are used: Streaming Systems, Data Lakes, and Data Warehouses. Streaming Networks like Amazon Kinesis and Apache Kafka, which focus on faster delivery, are used to convey Business Data.
The data is then gathered in Data Lakes such as Apache Hadoop or Amazon S3, which are designed for large-scale, low-cost storage. Unfortunately, Data Lakes do not provide the Performance or Quality required to support high-end business applications on their own; as a result, the most critical data is uploaded to Data Warehouses. These are optimized for significant Performance, Concurrency, and Security at a much higher storage cost than Data Lakes.
2) Lambda Architectures
Lambda Architecture is a typical method of preparing records in which a Batch System and a Streaming System prepare records in parallel. During inquiry time, the results are then blended to offer a complete answer. This Architecture became notable owing to the strict latency requirements for processing old and freshly produced events.
The Development and Operational Burden of maintaining two independent systems is the biggest disadvantage of this Architecture. Attempts to combine Batch and Streaming into a Single System have been made in the past. On the other hand, companies have not always been successful in their endeavors.
With the introduction of Delta Lake, many companies are implementing a simple Continuous Data Flow Architecture to analyze data as it arrives. The Delta Lake Architecture is what we name it. The main bottlenecks for adopting a Continuous Data Flow Model are discussed above, as well as how the Delta Lake Architecture addresses these issues.
3) Apache Hive on HDFS
Hadoop is built on top of Hive. It’s a Data Warehouse Framework for implementing and analyzing data collected in HDFS. A Hive is a piece of Open-Source Software that helps Hadoop programmers to analyze large data volumes. Records and Databases are created first in Hive, and data is subsequently organized into these Records.
Hive is a Data Warehouse that exclusively handles and queries Structured Data that is collected in Logs. MapReduce does not have the same optimization and usability features as UDFs when working with structured data, but the Hive framework does. In terms of performance, Query Optimization leads to a more efficient technique of Query Execution.
4) Apache Hive on S3
Amazon Elastic MapReduce (EMR) is a Cluster-based distributed Hadoop Framework that runs transparently, quickly, and cost-effectively on dynamically expandable Amazon EC2 instances to handle massive amounts of data. Apache Hive connects with data collected in Amazon S3 and runs on Amazon EMR Clusters.
A typical EMR Cluster will consist of a Master Node, one or more Core Nodes, and Arbitrary Task Nodes, each with its own set of Software resolutions capable of Shared Parallel Data Processing at scale. Delta Lake combines Data Science, Data Engineering, and Production Operations, making it ideal for the Machine Learning life cycle.
What makes Delta Lake different?
1) Spark ACID Transactions
Users will never view variable data thanks to Serializable Separation Levels. Several users would be accessing, i.e., reading and writing data in a typical Data Lake, and Data Integrity must be maintained. In the vast majority of Databases, ACID is a crucial feature. Still, when it comes to HDFS or S3, it’s difficult to provide the same level of reliability that ACID Databases do.
Delta Lake keeps track of all the commits made to the record directory in order to implement ACID Transactions in a Transaction Log. Serializable Isolation levels are provided by the Delta Lake Architecture to ensure Data Consistency across multiple users.
2) Scalable Metadata Options
It uses Spark’s Distributed Processing capability to easily manage all of the Metadata for Petabyte-Scale Records containing billions of files.
3) Streaming and Batching Systems
It is typical to employ Lambda Architecture in a Data Lake if you have a use case of both Stream Processing and Batch Processing. The Data coming in as Stream (maybe via Kafka) or whatever previous data you have (say HDFS) is the same record in Data Lake.
It provides a comprehensive view of both of these concepts. In Delta Lake, a record is both a Bunch and a Streaming Origin and Sink. Streaming Data Ingest, Batch Historic Backfill, and Interactive Queries are all available right away.
4) Schema Implementation
By implementing the power to specify the Schema and help execute it, Delta Lake helps prevent harmful data from entering your Data Lakes. It prevents Data Corruption by preventing faulty data from entering the system before it is ingested into the Data Lake and by displaying sensible Failure Signals.
5) Time Travel
When employing Delta Lake, Data Versioning provides for Rollbacks, Full Audit Trails, and repeatable Machine Learning processes.
6) Updation and Deletion
Change-Data-Capture, Slowly-Changing-Dimension (SCD) operations, Streaming Upserts, and other complex Use Cases are made possible by the Delta Lake Architecture, which offers merge, update, and delete operations.
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What is Delta Lake’s Transaction Log?
Transaction Records from the Delta Lake, commonly known as Delta Logs are chronological records of all transactions made on the Delta Lake table since its inception. Applications of the Transaction Log are:
- Allow several users to simultaneously read and write to the same table. Users are shown an accurate view of data. It keeps track of all changes made to the table by users.
- It implements Atomicity on Delta Lake, which means it monitors if transactions on your Delta Lake are complete fully or not at all. With the use of a Transaction Mechanism, Delta Lake ensures Atomicity. It also serves as a single truth source.
Working with Transaction Logs and Atomic Commits
- Every ten commits, Delta Lake generates Checkpoint Files automatically. The Checkpoint File maintains the current state of the Data in Parquet Format, which Spark can easily read.
- Since Delta Lake is built on top of Apache Spark, several users cannot simultaneously alter a table to solve this problem. When you need the same bits of data at the same time and there is a conflict, Delta Lake uses Optimistic Concurrency Control.
- Working with Transaction Logs and Atomic Commits in Delta Lake helps in resolving conflicts optimistically.
Enforcing Delta Lake Schema
Schema Validation is another name for Schema Enforcement. By matching data to be written on the table, the Delta Lake design ensures Data Quality. If the schema of the data does not match the schema of the table, the data is rejected.
Schema Enforcement is an extremely useful tool. It serves as a Checkpoint. It was used to feed data directly into Machine Learning Algorithms, Business Intelligence Dashboards, Data Visualization Tools, and a Production System that required strongly typed and Semantic Data.
Schema Evolution is a feature that allows users to easily change the current Schema of a table. It is most commonly used while appending or overwriting data. It can be used at any moment if you want to make a Table Schema Modification. After all, adding a new column isn’t difficult.
Delta Time Travel for Data Lakes
A new feature called Time Travel capability has been added. Apache-Spark is used to build the next-generation Unified Analytics Engine. All huge data stored in Delta Lake is automatically versioned by Delta.
All versions of the data are available to you. In the event of wicked writes, the Delta Lake Design streamlines the Data Pipeline by making it simple to alter and roll back data. You have two options for accessing Data Versions.
- Using a Timestamp: You can give the Data Frame reader a Timestamp or a Date String as an option.
- Using a Version Number: Every writer in Delta has a Version Number, which can be used to travel back in time.
Ingest Data into Delta Lake
You can use the COPY INTO SQL command allows you to load the data from a location into a Delta Table. The files which will be in the source location that has already been loaded will be skipped.
In the following example, you will create a Delta table and use the COPY INTO command to load the sample data from Azure Databricks datasets into the table.
For this Python language is being used where the notebook is attached to a Dataricks cluster. The following code is given below to create a Delta table.
table_name = 'default.loan_risks_upload'
source_data = '/databricks-datasets/learning-spark-v2/loans/loan-risks.snappy.parquet'
source_format = 'PARQUET'
spark.sql("DROP TABLE IF EXISTS " + table_name)
spark.sql("CREATE TABLE " + table_name + " ("
"loan_id BIGINT, " +
"funded_amnt INT, " +
"paid_amnt DOUBLE, " +
spark.sql("COPY INTO " + table_name +
" FROM '" + source_data + "'" +
" FILEFORMAT = " + source_format
loan_risks_upload_data = spark.sql("SELECT * FROM " + table_name)
| loan_id | funded_amnt | paid_amnt | addr_state |
| 0 | 1000 | 182.22 | CA |
| 1 | 1000 | 361.19 | WA |
| 2 | 1000 | 176.26 | TX |
If you want to clean up and delete the following table. Then run the command given below.
spark.sql("DROP TABLE " + table_name)
Best Practices of Delta Lake
Some of the best practices to follow when using Delta Lake are listed below:
Provide Data Location Hints
If you are expecting to use a column more often in query predicates and if that column has high cardinality then you should use Z-ORDER BY. Delta Lakes can automatically layout the data in the files based on the value of the column and use the information to skip the irrelevant data while querying.
Choose the Correct Partition Column
The most common way to partition a column is based on DATE. You should not use the column for partitioning if the cardinality of the column is very high. Also, you can partiti0on the column if you are expecting data that needs to be partitioned to be at least 1GB.
Continuously adding data in small batches to the Delta table will adversely affect the efficiency of the table reads and the performance of the file system. A large number of small files should be rewritten into a smaller number of larger files regularly. This is known as compaction.
You should not use the Spark Caching in case you lose data which can come from the additional filters added on the top of the cached DataFrame.
The structure of data changes over time as business concerns and requirements change. However, with the help of Delta Lake, adding new Dimensions as the data changes are simple. Delta Lakes improve the performance, reliability, and manageability of Data Lakes. As a result, use a secure and scalable Cloud Solution to improve the Data Lake’s Quality. In case you want to ingest data into your desired Database/destination, then Hevo Data is the right choice for you!
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