What is ETL?
"Extract, transform, and load" is the acronym for "extract, transform, and load."
In data integration techniques, the ETL procedure is crucial. Businesses can use ETL to collect data from a variety of sources and combine it into a single, centralized location. Different sorts of data can also operate together with the help of ETL.
Data can also be migrated between a variety of sources, destinations, and analytic tools using ETL. As a result, the ETL process is essential for generating business intelligence and carrying out larger data management initiatives.
Functioning of ETL
The ETL consist of the 3 basic steps, they are Extract, Transform and Load. By performing these 3 steps we extract the data and after processing it we store it to a particular destination.
Step 1: Extraction
Only a small percentage of businesses rely on a single data type or technology. To develop business information, most organizations manage data from a range of sources and employ a variety of data analysis methods. Data must be allowed to move freely between systems and apps in order for a complicated data strategy like this to work.
Data must first be retrieved from its source before being relocated to a new location. In the very first stage of the ETL process, the structured and unstructured data are imported and merged into a single storage place or database. The extraction of the data can be done from a variety of source and by keeping them in their original form are-
Although it is possible to extract data by hand, it is time-consuming and prone to errors. ETL tools automate the extraction process, resulting in a more dependable and efficient workflow.
Step 2: Transformation
In this step we can also do the data validation. This will ensure that all the data that are being transformed as per the need is correct. We maintain the quality of the data during this ETL process. We can also use regulations to assist our organization in meeting its reporting obligations. There are various sub-processes in the data transformation process:
Transformation is widely regarded as the most crucial step in the ETL process. Data transformation increases data integrity and ensures that data is fully compliant and ready to use when it arrives at its new location.
Step 3: Loading
Now in this step, the freshly transformed data is loaded into a new destination as the final step in the ETL process. Data can be loaded in bulk (full load) or at predetermined intervals (incremental load).
Full loading - Everything that comes off the transformation assembly line gets into new, unique entries in the data warehouse in an ETL full loading scenario. Though this may be advantageous for research purposes at times, complete loading results in data sets that increase exponentially and become difficult to manage.
Incremental loading - The Incremental loading process is a less comprehensive but more controllable technique. Incremental loading compares new data to what's already on hand, and only creates new records if new and unique data is discovered. This architecture enables business intelligence to be maintained and managed by smaller, less expensive data warehouses.
ETL and business intelligence
Companies now have access to more data from more sources than ever before, making data strategies more complex than ever. ETL allows large amounts of data to be transformed into useful business intelligence.
Consider the quantity of data a company has access to. The organization collects marketing, sales, logistics, and financial data in addition to data collected by sensors in the facility and machines on an assembly line.
To be analyzed, all of that data must be removed, altered, and loaded into a new location. In this case we can use the ETL method in order to get the best out of our data by -
Delivering a single point-of-view
Multiple data sets need time and coordination, which can lead to inefficiencies and delays. Databases and diverse types of data are combined into a single, cohesive view using ETL.
Providing historical context
ETL helps a company to merge data from old platforms and apps with data from new platforms and applications. This creates a long-term picture of data, allowing older data sets to be compared to more recent ones.
Improving efficiency and productivity
Hand-coded data migration is made easier with ETL Software. As a result, developers and their teams may focus more on innovation and less on the time-consuming chore of creating data-moving and formatting code.
Building The Right ETL strategy for Migration of DATA
There are 2 different ways in which we can easily do the ETL process. The one way or other , both have their own requirement and costing. Businesses may delegate the development of their own ETL to their developers in various instances. However, this procedure is time-consuming, prone to delays, and costly.
Most firms now use an ETL tool, in order to smoothly do there data integration process. ETL tools are noted for their speed, scalability, and cost-effectiveness, as well as their ability to integrate with larger data management techniques. ETL tools also come with a variety of data quality and governance features.
We'll also need to decide whether an open-source product is appropriate for our company, as they often offer more freedom and assist users avoid vendor lock-in.
Talend Data Fabric is a set of apps that connect all of our data, regardless of its source or destination.
The data is gathered, cleansed, and processed in a standard place. Finally, the data is placed into a datastore and queried from there. Import data is processed by legacy ETL, which cleans it in place before storing it in a relational data engine.
A wide range of Apache Hadoop environment components are supported by Azure HDInsight for ETL at scale.
The parts that follow go over each of the ETL steps and their components.
Orchestration is used at every stage of the ETL pipeline. In HDInsight, ETL processes frequently entail the use of several separate products in tandem. Consider the following scenario:
Orchestration is required to run the right job at the right time. It is very important part which must be taken care of.
Apache Oozie is a Hadoop task management workflow coordination framework. Oozie is a Hadoop integration that operates within an HDInsight cluster.
Azure Data Factory
In the guise of platform as a service, Azure Data Factory delivers orchestration capabilities (PaaS). Azure Data Factory is a data integration solution that runs in the cloud. It helps us to orchestrate and automate data transportation and transformation using data-driven workflows.
Storage of input files and output files
Mostly the source data files are directly uploaded to Azure Storage or Azure Data Lake Storage which is one of the most easy method. Typically, the files are in a flat format, such as CSV or any other format which is supported in the Azure, although it supports a very vied variety of data formats.
Specific adaptation goals have been set for Azure Storage. When dealing with a large number of tiny files, Azure Storage scales best for most analytic nodes. Azure Storage provides the same speed regardless of file size as long as it stay within our account limitations. Terabytes of data can be stored with consistent performance.
For storing web logs or sensor data, we can easily use a blob storage. The blob storages are available in a variety of shape and sizes, we can opt them out as per our needs.
They can be dispersed among multiple servers easily, if we want to scale out access to many blobs. A single blob, on the other hand, is serviced by a single server.
For blob storage, Azure Storage includes a WebHDFS API layer. All files can be accessed by the HDInsight for any type of cleansing process or any other process for which data is required from this storage. This is comparable to how Hadoop Distributed File System would be used by those services (HDFS).
ADLS or Azure Data Lake Storage
The Azure Data Lake Storage is also abbreviated as ADLS. It is a managed, hyperscale analytics data store which is used to store the data. It's compatible with HDFS and follows a similar architectural approach. Data Lake Storage allows for infinite flexibility in terms of total capacity and file size.
Azure Data Factory is typically used to feed data into Data Lake Storage. Data Lake Storage SDKs, the AdlCopy service, Apache DistCp, and Apache Sqoop are also options. The service we select is determined by the location of the data.
Event ingestion via Azure Event Hubs or Apache Storm is suited for Data Lake Storage.
Azure Synapse Analytics
To save prepared results, Azure Synapse Analytics is a good option.
Azure Synapse Analytics is a relational database store designed specifically for analytical workloads. It uses partitioned tables to scale. Multiple nodes can be used to split tables. We need to choose the nodes according to our need at the time of the creation of the Azure Synapse Analytics. They can scale afterward, but this is an active process that may necessitate data movement.
In the Microsoft Azure we have a special functionality which is the Apache HBas, it is a key-value store which is present in the Azure HDInsight. It's a free, open-source NoSQL database based on Hadoop and inspired by Google BigTable. For vast volumes of unstructured and semi-structured data, HBase enables performant random access and robust consistency.
We don't have to define columns or data types before using HBase because it's a schemaless database. Data is organized by column family and stored in the rows of a table.
In order to handle petabytes of data the open-source code scales linearly across thousands of nodes.
HBase relies on distributed applications in the Hadoop environment to provide data redundancy, batch processing, and other functionalities.
For future analysis, HBase is a useful place to save sensor and log data.
Azure SQL databases
There are three PaaS relational databases available in Azure:
To scale these items, add more CPU and memory. Premium disks can also be used with the items for improved I/O performance.
Apache Sqoop is a tool for moving data across structured, semi-structured, and unstructured data sources quickly and efficiently.
Sqoop imports and exports data using MapReduce, which allows for parallel processing and fault tolerance.
Apache Flume is a service for rapidly collecting, aggregating, and transporting huge amounts of log data that is distributed, reliable, and available. Its adaptable architecture is built on the concept of streaming data flows. Flume has configurable reliability methods that make it robust and fault-tolerant. It offers a lot of failover and recovery features.
Flume employs a straightforward extensible data model that enables online analytic applications.
Azure HDInsight does not support Apache Flume. In order to transfer data to Azure Blob storage or Azure Data Lake Storage from an on-premises Hadoop installation the flume can be used to do the task easly.
After the data has been stored in the desired location, it must be cleaned, combined, or prepared for a certain use pattern.