Apache Spark is the top-notch distributed data processing framework and analytics engine that helps you to ETL(Extract, Transform and Load) very easily.
ETL- addresses the data transformation (cleansing and aggregation) need between data sources (not optimized for analytics) and the destination (Database/Data Warehouse/Data Lake).
- Extract: pulling data from the source(original database or data source).
2. Transform(Staging): changing the structure of the information, so it integrates with the target data system and the rest of the data in that system.
3. Load: Loading refers to the process of storing the information into a target database.
New ideas emerge every day rooting for new applications and digital transformations, which in turn are generating huge amounts of data leading to New challenges like
- Diversity of data sources (Cloud, SaaS, IoT, internet).
- need of increased data volumes to be processed.
- structured, semi-structured, and unstructured data.
Taking advantage of data is decisive to answering many challenging business problems, Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises.
How quickly can ingest, clean, and transform this mass of data?
Spark to the rescue-
Spark ETL delivers clean data. loading of petabytes and conversion between a variety of data types is easy with Spark ETL.
let's see why spark shines when it comes to ETL.
Flow- in ETL, data moves from the data source to staging and then into the data warehouse. here the staging part is where Spark comes into action.
Spark ETL can ingest data from a wide variety of sources and handle incorrect, incomplete, and inconsistent inputs, and produce curated, consistent output for downstream application use.
Security- ETL can help with data privacy and compliance by cleaning sensitive and secure data even before loading it into the data warehouse, most of the computations that happen in Spark are in-memory, so we can rely on spark for this.
Cost- sophisticated data transformations can be more cost-effective.
Speed- Spark runs 100 folds faster than Hadoop and other big data engines.
Ease of use- Let's check out the most common types of transformations and how they work under the hood to understand spark ETL:
Basic transformations:
- Cleaning: Mapping NULL to 0 or “Male” to “M” and “Female” to “F,” date format consistency, etc.
- Deduplication: Identifying and removing duplicate records.
- Format revision: Character set conversion, unit of measurement conversion, date/time conversion, etc.
- Key restructuring: Establishing key relationships across tables.
Advanced transformations:
- Derivation: Applying business rules to your data that derive new calculated values from existing data — for example, creating a revenue metric that subtracts taxes.
- Filtering: Selecting only certain rows and/or columns.
- Joining: Linking data from multiple sources — for example, adding ad spend data across multiple platforms, such as Google Adwords and Facebook Ads.
- Splitting: Splitting a single column into multiple columns.
- Data validation: Simple or complex data validation — for example, if the first three columns in a row are empty then reject the row from processing.
- Summarization: Values are summarized to obtain total figures which are calculated and stored at multiple levels as business metrics.
- Aggregation: Data elements are aggregated from multiple data sources and databases.
- Integration: Give each unique data element one standard name with one standard definition. Data integration reconciles different data names and values for the same data element.
Conclusion
Apache Spark is a very popular and demanding Big Data tool that helps to write ETL very easily. You can load Petabytes of data and can process it with ease by setting up a cluster of multiple nodes.
Hands-on examples on ETL will be published in upcoming post.
