Setting Up ETL in Hadoop: 5 Easy Steps

A Data Warehouse is useful in storing and processing data that can be further used for analysis by other applications and numerous Business Intelligence (BI) tools. This article aims at showing you how to extract any type of data from different sources, transform it into your desired form, load it using ETL in Hadoop, and thereby create a data warehouse.

You can use this data in the data warehouse to get insights through further analysis. In this blog, you will be introduced to the concept of Extract, Transform, and Load (ETL), Hadoop, the features of Hadoop, and how to set up ETL in Hadoop. Subsequently, essential tips to consider before using ETL in Hadoop will also be discussed.

Introduction to ETL

Extract, Transform, and Load (ETL) is a form of the data integration process which can blend data from multiple sources into data warehouses. To save time companies use automated ETL tools such as Hevo, an automated Data Pipeline solution for loading data without writing a single line of code that seamlessly connects to all popular Databases and Data Warehouses such as Google BigQuery, MySQL, Snowflake, etc.

Introduction to Hadoop

Hadoop is an open-source software framework, used in the processing and storage of data for big data applications in clusters of computer servers built from commodity hardware. It provides massive storage for any kind of data, enormous processing power, and can take concurrent tasks or jobs by using parallel processing. It is the bedrock of big data technologies that support advanced analytics initiatives, including predictive analytics, data mining, and machine learning. 

The Hadoop platform has tools that can extract the data from source systems, such as log files, machine data, or online databases, and load them to Hadoop in record time. It is also possible to do transformations on the fly. Complex ETL jobs are deployed and executed in a distributed manner due to the programming and scripting frameworks on Hadoop.

The core of Hadoop consists of a storage part, known as the Hadoop Distributed File System (HDFS), and a processing part, which is a MapReduce programming model. The Hadoop-based framework is composed of the following modules:

• Hadoop Distributed File System (HDFS): It is a distributed file system that allows data to be stored in an easily accessible format, across a large number of clusters and a proper Schema design is required for the HDFS ETL process.
• Hadoop MapReduce: As the name implies, it carries out two basic operations. It reads data from the database, maps them into a suitable format for analysis, and then implements the MapReduce programming model for large-scale data processing.
• Hadoop Common: It contains libraries and utilities needed by other Hadoop modules.
• Hadoop YARN (Yet Another Resource Negotiator): It is responsible for managing computing resources in clusters and running the analysis.

Key Features of Hadoop

Hadoop has the following features:

• It can process any kind of data, be it structured or unstructured, and store vast amounts of data quickly.
• Hadoop has a high computing power because of its computing model. Using a significant number of computing nodes results in high computing power.
• Data and applications are protected against failure. If a node becomes faulty, the computing is distributed to other functional nodes to ensure continuation, and the data is backed up automatically by the active operating nodes.
• Flexibility on Hadoop is one of its main features. You can store any type and amount of data as you want, and decide how to use it later. The data does not have to be processed before storing it.
• Scalability is also a forte in Hadoop. When your data begins to grow, you can add more nodes to your system to handle them. 
• The open-source framework is free and has a low cost of usage, which makes it widely accepted.

To learn more about Hadoop, click here.

Key Parameters when Setting Up ETL in Hadoop

It is important to consider the following points before you set up an ETL in Hadoop:

• You can use Views when you have a transactional system where data is continuously changing and use tables when data is not changing.
• In Hadoop, mapping data is far cheaper than partitioning the data based on a query, especially in transactional data.
• It is advisable to program in parallel to maximize Hadoop’s processing power. It can be done by breaking logic into multiple phases and running these steps in parallel to speed up your ETL in Hadoop.
• Use Managed Tables in Hadoop as they are better to govern except when you are importing data from the external system, in such a case, you can use External Tables. After that, you need to define a schema for it, and the entire workflow creates using Hive scripts.
• Phase Development is also useful while setting up the ETL process in Hadoop. All you need to do is keep parking processed data into various phases and then keep treating it to obtain a final result.
• Pig and Hive are commonly used by Hadoop vendors (Cloudera, Hortonworks, etc.). Using Hive can be quite complicated compared to Pig because of the logic, but there are very few people who know Pig. You would need resources in the future to maintain the code so the solution you choose matters. Various industries are using Hive, and companies like Hortonworks, AWS, MS, etc. are contributing to Hive.

Setting up ETL in Hadoop

Now that you have understood about ETL process and Hadoop. In this section, you will go through the steps to set up ETL in Hadoop. For setting up ETL in Hadoop, you have to follow these five steps:

1. Setting up a Hadoop Cluster
2. Connecting Data Sources
3. Defining the Metadata 
4. Creating Jobs for ETL in Hadoop
5. Creating the Workflow for ETL in Hadoop

1. Setting up a Hadoop Cluster

The first step of setting up ETL in Hadoop requires you to build a Hadoop cluster and decide where you want to create your cluster. It can be locally in an in-house data center or in the cloud, depending on the type of data you want to analyze. If it is an in-house data center, you will have to consider whether the data can be moved to the cloud subsequently and can test data used for development.

For data clusters on the cloud, such as Cloudera, Hortonworks, MapR, Amazon Elastic Map Reduce, Altiscale, Microsoft, Rackspace CBD, or other Hadoop cloud offerings, it can be done in a few clicks.

2. Connecting Data Sources

The Hadoop ecosystem has varieties of open-source technologies that complement and increase its capacities. They enable you to connect different data sources after setting up your ETL in Hadoop.

The data sources could be a database, Relational Database Management System (RDBMS), machine data, flat files, log files, web sources, and other sources such as RDF Site Summary (RSS) feeds. The ETL tools for connecting these data sources include Apache Flume and Apache Sqoop, Apache HBase, Apache Hive, Apache Oozie, Apache Phoenix, Apache Pig, and Apache ZooKeeper.

While setting up ETL in Hadoop, you have to plan your data architecture depending on the amount of data, type, and rate of new data generation. You can start small and increase the project as it advances from stage to stage, knowing that your aim is moving data into Hadoop at a frequency that meets your analytic requirements. When combined with business intelligence tools, it gives more significant insights. 

3. Defining the Metadata

Even though you can store data in Hadoop and decide how to use them later, it is essential to define the semantics and structure of data for analytics purposes. The classification process will help you in the transformation of data as you desire by defining the metadata.

You can remove the ambiguity from ETL in Hadoop, of how a field looks and generates, with a transparent design and documentation. For example, you can define a field – student ID in the warehouse either as a five-digit numeric key, generated by an algorithm or as a four-digit sequence number that appends to an existing ID.

4. Creating Jobs for ETL in Hadoop

Now to execute your ETL in Hadoop, you need to focus on the process of transforming the data from various sources. Technologies, such as MapReduce, Cascading, Pig, and Hive are some of the most commonly used frameworks for developing ETL jobs. Deciding which technology to use and how to create the jobs depends on the data set and transformations. 

Identify if your data extraction is a batch job or a streaming job. A batch job for ETL in Hadoop takes the whole file, processes it, and saves it to a larger file. A streaming job takes data from a Relational Database Management System (RDBMS), where the data transfers separately one after the other for further processing. Various ETL systems cope with these tasks in different ways.

Nowadays, the batch-only approach is less in use because of the growing number of streaming data sources available for stream jobs. This way your ETL in Hadoop also makes the most recent data available as quickly as possible.

5. Creating the Workflow for ETL in Hadoop

This is the final step of setting up ETL in Hadoop. Creating a workflow with multiple ETL jobs, each carrying out a specific task, helps in the transformation and cleansing of data efficiently. These data mappings and transformations execute in a particular order.

There may also be dependencies to check, and these dependencies are captured in the workflow. Parallel workflows result in parallel execution of data, thereby speeding up the ETL process. A smooth workflow can be derived by scheduling it to run hourly, nightly, weekly, or as frequently as you so wish.

After you have done all of this, a data warehouse is created, and the data will be ready for analysis. Hive, Impala, and Lingual provide SQL-on-Hadoop functionality along with several commercial BI tools that can connect to Hadoop to explore the data and generate visually appealing reports.

That’s it! You have now set up ETL in Hadoop successfully.

ETL vs ELT in Hadoop

The ETL process is the backbone of all the Data Warehousing tools. ETL in Hadoop solved the prominent problems of data i.e. Velocity, Volume, and Variety. In this section let’s have a look at the ETL vs ELT process on Hadoop. 

ETL tools have been serving the Data Warehouse needs but the changing nature of the data forced organizations to shift to Hadoop. ETL in Hadoop is a cost-effective and scalable solution. 

ELT on Hadoop delivered flexibility in a data processing environment. Shifting from the traditional ETL process to ELT on Hadoop is a challenge for organizations but ELT on Hadoop is a better choice in the long term. 

The ELT in Hadoop separates the loading and transformation tasks into independent blocks making project management easier whereas ETL in Hadoop loads the important data, as identified at design time. 

Manually doing an ETL or ELT process can be a tiresome task. Hevo supports both ETL and ELT process that allows you to automate the process in a matter of a few clicks.

Conclusion

This article contains lessons on how to set up an ETL in Hadoop. You learned about what is ETL in Hadoop in detail.

Although Hadoop is a useful big data storage and processing platform, it can also be limiting as the storage is cheap, but the processing is expensive. You cannot complete a job in sub-seconds as it takes a longer time. It is also not a transactional system as source data does not change, so you have to keep importing it over and over again. However, setting up in-house ETL in Hadoop demands technical proficiency. Furthermore, you will have to build an in-house solution from scratch if you wish to transfer your data from any source to Hadoop or another Data Warehouse for analysis.

Hevo is an all-in-one cloud-based ETL pipeline that will not only help you transfer data but also transform it into an analysis-ready form. Hevo’s native integration with 150+ data sources (including 40+ free sources) ensures you can move your data without the need to write complex ETL scripts. Hevo’s Data Pipeline provides a fully automated and secure data transfer without having to write any code. It will make your life easier and make data migration hassle-free.

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