Hadoop - Best Practices and Insights

Hadoop, an open-source framework has been around for quite some time in the industry. You would see a multitude of articles on how to use Hadoop for various data transformation needs, features, and functionalities.

But do you know what are the Hadoop best practices for ETL? Remember that as this is an open-source project, there are many features available in Hadoop. But it may not be the best option to use them in new Hadoop ETL development. I have seen from experience that old database programmers use Oracle/SQL Server’s techniques in Hive and screw up the performance of the ETL logic.

Hadoop has become a central platform for ETL workloads in big data systems, especially when scaling transformations across structured, semi‑structured, and unstructured data. Modern etl best practices encourage distributed parallel processing rather than traditional row‑by‑row operations because Hadoop’s architecture supports data‑parallel jobs across clusters. 

So, why do these techniques exist when it is not in your best interest to use them? It is because of Hadoop’s open-source nature and competition in the industry. If a client is doing a Teradata conversion project, they can save enough dollars by just converting Teradata logic to Hive, and for performance gain, they don’t mind paying for additional hardware. This is why all the features of all the traditional databases exist in Hive. 

The Dos and Don'ts

If you are writing a new logic in Hadoop, use the proposed methodology for Hadoop development.

Do not use Views

Views are great for transactional systems, where data is frequently changing, and a programmer can consolidate sophisticated logic into a View. When source data is not changing, create a table instead of a View.

Do not use small partitions

In a transactional system, to reduce the query time, a simple approach is to partition the data based on a query where clause. While in Hadoop, the mapping is far cheaper than start and stop of a container. So use partition only when the data size of each partition is about a block size or more (64/128 MB).

Use ORC or Parquet File format

By changing the underneath file format to ORC or Parquet, we can get a significant performance gain.

Avoid Sequential Programming (Phase Development)

We need to find ways, where we can program in parallel and use Hadoop’s processing power. The core of this is breaking logic into multiple phases, and we should be able to run these steps in parallel.

Managed Table vs. External Table

Adopt a standard and stick to it but I recommend using Managed Tables. They are always better to govern. You should use External Tables when you are importing data from the external system. You need to define a schema for it after that entire workflow can be created using Hive scripts leveraging managed tables.

Pig vs. Hive

If you ask any Hadoop vendor (Cloudera, Hortonworks, etc.), you will not get a definitive answer as they support both languages. Sometimes, logic in Hive can be quite complicated compared to Pig but I would still advise using Hive if possible. This is because we would need resources in the future to maintain the code.

There are very few people who know Pig, and it is a steep learning curve. And also, there is not much investment happening in Pig as compared to Hive. Various industries are using Hive and companies like Hortonworks, AWS, MS, etc. are contributing to Hive.

Phases Development

So how would you get the right outcome without using Views? You would get that by using Phase Development. Keep parking processed data into various phases and then keep treating it to obtain a final result.

Basic principles of Hadoop programming

1. Storage is cheap while processing is expensive.

2. You cannot complete a job in sub-seconds, it would take way more than that, usually a few seconds to minutes.

3. It’s not a transactional system; source data is not changing until we import it again.

Designing Hadoop ETL with Efficient Tools and Workflow 

In modern hadoop for etl development, it is important to design ETL pipelines that take advantage of distributed computing power rather than mimicking traditional database ETL patterns. Tools like Apache Pig, Cascading, and workflow schedulers such as Apache Oozie help orchestrate complex transform and load steps within Hadoop. 

Effective etl best practices in Hadoop also emphasize choosing efficient data formats such as ORC and Parquet, which reduce storage cost and speed up query performance while maintaining transform flexibility. 

Additionally, ongoing testing of Hadoop ETL pipelines — including checking data accuracy, completeness, and performance under large scale loads — is essential to maintain quality as data volumes grow. This includes validating transformations on distributed jobs using sampling or automated validation tools.

Emerging ETL Trends and Hadoop 

As enterprises move beyond batch hadoop etl, hybrid pipelines incorporating real‑time data ingestion and transformation are becoming more common. This reflects a broader shift in ETL strategies where data arrives from streaming sources and must be transformed on the fly before loading. 

Another emerging trend is the integration of data catalogs, lineage tracking, and metadata tools that provide observability and governance over Hadoop‑based pipelines. This helps ensure repeatability and compliance across complex ETL ecosystems. 

Modern cloud Hadoop solutions (like EMR or managed Hadoop environments in the cloud) can further optimize ETL workflows by offering scalable compute on demand and easier integration with BI and analytics tools.

Hadoop ETL Architecture Best Practices 

When building hadoop for etl, architectural best practices advise separating extraction, transformation, and load stages into modular components. Each component should be:

• Extract from source systems efficiently via connectors (e.g., Sqoop, Kafka).
• Transform data using MapReduce, Apache Spark, or Hive SQL based on volume and latency needs.

Load transformed data into target tables or data lakes where downstream processing occurs.
These practices improve maintainability and scalability.

Conclusion

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FAQ’s

1. What is Hadoop ETL and why is it important?
   Hadoop ETL refers to using Hadoop ecosystem components to extract data from sources, transform it at scale, and load it into analytics platforms or data lakes. It is crucial for big data processing where traditional ETL tools struggle with volume and variety. 
2. What are common ETL best practices in a Hadoop environment?
   Best practices include using columnar formats (ORC/Parquet), parallel processing frameworks (Spark/MapReduce), efficient partitioning, and automated pipeline testing. 
3. Which tools are commonly used for Hadoop ETL workflows?
   Tools such as Apache Pig, Hive, Cascading, Sqoop, and workflow schedulers like Oozie are widely used in Hadoop ETL development for orchestration and transformation logic. 
4. How does Hadoop ETL handle large volumes of data?
   Hadoop uses distributed storage (HDFS) and parallel compute engines to process extremely large datasets efficiently, often more effectively than traditional systems. 
5. What is the difference between Hadoop ETL and traditional ETL?
   Traditional ETL is usually centralized and batch‑oriented, whereas Hadoop ETL leverages distributed processing and scalable storage to handle large, diverse datasets and complex transformations.