Data Processing with EtLT: A Modern Architectural Shift

Introduction to EtLT 

This is the process of extraction, transformation, and loading, an outdated but fundamental process in moving and processing data. The inclusion of such a process would entail extraction from sources, probable transformation, and then loading into the Data Warehouse that is focused on use. 

However, when those volumes began to increase and receive real-time processing, some common ETL issues emerged. In modern data, ETL pipes will never be able to handle the volume or velocity pressure that is put on them. 

Nowadays, the trend is towards following EtLT-Extract, Light Transform, Load, and Transform. Rather than going on to do all of the transformations from the very first step of the process, it makes it possible for a two-step loading of data where a preliminary light transformation takes place at the very beginning of it, followed by processing complex transformation after loading. 

This change in architecture, therefore, leaves more options on how to handle the data in real-time as it streams or even in batches. As a principle, it does pretty well with cloud storage, which is key in scaling. Due to this, EtLT has become quite welcomed and is generally believed to be the new way of doing systems in modern data processing. 

Breaking Down EtLT 

Now, let’s examine each stage of the EtLT process and explain how it works and why it is effective. It is time to consider the whole process of the EtLT to understand the working of the model and its efficiency at the same time.

E (Extract)

This is the first step in data gathering from a large number of sources available today. It can run the whole setup, from normal Structured Databases to Structured APIs and more contemporary and fast real-time streaming sources like Apache Kafka. Here, the objective remains just that—one thing and only one: it has not really focused on great data collection without loading until this present time.

t (tweak light transform)

After extraction, this data is only slightly pre-processed. In other words, they could remove a few unnecessary fields, adjust the fields’ order, or modify the structure itself. The fact is that it is only a method to prepare data for other analyses with minimal possible complexity without overcomplicating everything. This also enables the process to be shortened while preserving the data that needs to be processed at a later stage.

L - Load

This means data ingestion to storage architecture. The concept of big data ingestion makes this distinction possible by feeding data into storage infrastructure. It can be the data warehouse, snowflake architecture, AWS S3 cloud storage, or data lake in Delta Lake. Even more so, the user who would like to make queries has the data uploaded into the system which has been prepared in such a manner that data can already be processed with speed at the same time.

T - Transform

Perform any computation-intensive transformations as a final touch after the data is stored in the storage system. Some of such transformations include aggregation, business-specific logic, or joining two or more datasets. Delay intricate transformations up to this stage of the pipeline since they present a very small risk of slowing down the initial processing pipeline. 

EtLT: Why It Fits Data Demands Today 

Compared to traditional ETL, in today's high-velocity data environment, EtLT offers the following principal advantages: 

1. Scalability

   It is perhaps the most often cited advantage of EtLT that storage and computation resources increase or scale separately. 

   ETL, in its traditional sense, uses very heavy transformations, slowing down the data pipeline, especially when dealing with volume. However, EtLT emphasizes that raw data or only slightly processed data are stored up front. This storage ability is scalable and does not experience bottlenecking instances arising from the complexity of the transformation process. Transformations can then be done if and when needed with full consistency in the performance and scalability of your pipeline matched up against the growth of your data. 

2. Supports Real-Time and Batch Processes
   

   Real-time data processing within traditional ETL is fairly tough, as the nature of the steps used for transformation makes it delayed. 

   Still, with EtLT, we can take real-time data, such as weblogs, sensor readings, or social media feeds, lightly transform that information, and load it into your system without creating latency. 

   Meanwhile, batch data (such as nightly sales reports) can be parallelized, which increases overall throughput without impacting real-time workloads. Therefore, EtLT is capable of handling both real-time and batch data processing. 

3. Cloud-Native
   

   EtLT is in the right direction of cloud-native architectures. Most clouds, AWS, Google, Azure, or their equivalents, offer deep archival storage for deep archiving. It is a manner of stashing huge amounts of 'cold' data in an economic way. 

   This makes it conceivable that information may be stored inside a cloud setup with the basic attributes of storing data, and further processing can be done within the cloud using appropriate cloud applications. The entire process, thereby, becomes cheaper and more flexible to demand varied data. 

4. Increased Flexibility
   Besides, the two-stage transformation process in EtLT implies that changing and improving your pipeline is easier as your data needs are transformed over time. While all transformation activity is done at the time of ETL, one major bottleneck in the case of ETL design in business process change will be the required change in ETL transformation, which is a highly cumbersome and error-prone task. 
   Whereas this process of change can be governed and enacted at a later stage with the use of the transforming context of EtLT, no resubmission of the data will be required for those cases, nor is emergence needed. 

Practical Exercise: Development of an EtLT Pipeline - Step by Step 

With those essential concepts now in place, I want to take you through how we might go about building an EtLT pipeline. For this design, we will use Apache Kafka, Apache Spark, and Delta Lake, all off-the-shelf tools. 

Items You Need

• Apache Kafka: A publish-subscribe messaging application designed for real-time data streaming. 

• Apache Spark: A fast and general computational engine for big data. 

• Delta Lake is a highly scalable storage layer that gives users the ability to perform fast queries as well as real-time ingest. 

Step 1: Reading from Kafka 

The data source of the EtLT pipeline is a Kafka stream from which real-time data is extracted. In fact, Kafka is extremely optimized for data that is natively event-streams, such as website logs, IoT sensors, and financial transactions. Here is the code snippet that could do it using Apache Spark. Here is the code snippet that could do it using Apache Spark: 

# Read from Kafka Stream using Spark 

df = spark \ 

.readStream \ 

.format("kafka") \ 

.option("kafka.bootstrap.servers", "localhost:9092") \ 

Option("subscribe", "topic_name") \ 

load() 

Step 2: Light Transformation 

After extracting the data from Kafka, we perform some minimal transformations. Transformations, in this case, represent removing unwanted rows or cases of missing data or changing fields into appropriate data types. This stage is also supposed to make the approach straightforward, as this will lower the pace in the pipeline if it's not. 

# Filter data and Cast it 

transformed_df = df.select("key", "value").selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") 

df.loc[df['desired_column'] == 'desired_value'] 

Step 3: Loading the Data into Delta Lake 

After the transformation from the initial form, data will be ready to load into any other solution for storage, such as Delta Lake. Delta Lake supports scalable storage, which provides real-time querying. The next section explains how to load data into the Delta Lake table using Apache Spark: 

#Writing Data to Delta Lake 

transformed_df.write.format("delta").mode("overwrite").save("/mnt/delta/events/") 

Step 4: Apply the Final Transforms 

However, once the data is securely stored in the Delta Lake, it creates other higher-level transformations. Imagine that you are to find out how many activities in some time slices were conducted by each user. You could do this aggregation once the data has been loaded. You could do this aggregation once the data has been loaded: 

# Make the last modification using SQL 

final_df = spark.sql("”” 

SELECT user_id, COUNT(*) AS action_count 

FROM delta.`/mnt/delta/events/` 

Group by user_id 

""" 

This will break down the operations so that our pipeline remains effective, even when there is a lot of data or complex transformations.