With the growth of data types, amount of data, and data sources in an organization, the demand for using this data in analytics is growing as well. The importance of data engineering, and data science is growing to fulfill business insights. To prioritize these insights, the data engineers are under high pressure because they are responsible for analytics. To ease these analytics, the ETL tools are helpful.
Data Engineering and ETL
ETL stands for extract, transform, and load. It is a process responsible for moving data from one database to other multiple databases, or a data warehouse. It makes data analysts provide functional business information, and prepare data effectively for analysis, and business intelligence.
Data engineering incorporates ETL to prepare data by using multiple systems. It involves assimilating, converting, delivering, and sharing data for analysis. For this purpose of process automation, it uses data pipelines (a set of elements to process data, move data from one source to another, and from one format to another).
Top 10 ETL Tools for Data Engineering in 2022
Data engineering tools are the specified applications to build data pipelines and design easy and efficient algorithms. These tools make the daily tasks of a data engineer easy. The top ten tools for data engineering in 2022 are described below:
1. Apache Spark
Apache Spark is a data analytics tool to process data on large scales. It is an open-source engine that includes Apple, IBM, Microsoft, etc. as a customer base.
Core Features of Apache Spark
- A fast platform for stream processing and data management
- Efficient for big data engineering
- It can handle data in real time with stream processing
- It allows processing 100 times faster
- It uses RDD to make data processing faster
Pros of Apache Spark
- It supports computational design and graph processing
- It fits best in iterative calculations
- It is more flexible because it can access unstructured and structured data from different sources
Cons of Apache Spark
- It does not contain any file management system
- It takes up much RAM and memory usage is high
- It is costly, as it needs additional memory for execution
2. AWS Glue
AWS Glue enables the transfer of streaming data to the data warehouse clean, verified, and organize. It is best for companies that are using SQL databases.
Core Features of AWS Glue
- It processes semi-structured data and also
- Works best with SQL database
- ETL jobs are easily schedulable with it
- It scales the required storage resources automatically
Pros of AWS Glue
- Free for the first million objects’ storage
- It has third-party APIs also
- Allows faster integration of data
Cons of AWS Glue
- A server-less application
- Some aspects are not friendly to non-technical users
- Supports Python and Scala only
3. Oracle Data Integrator
ODI is a powerful platform for large businesses. It is responsible to transfer data from one point to the entire business function of the company.
Key Features of ODI
- Supports integrated workflows
- Process requests for data integration
- Supports parallel execution of tasks
- Process both unstructured and structured data
Pros of ODI
- Supports RDBMS (relational database management system)
- It has third-party APIs
- Supports Sqoop, Spark Streaming, HBase, and Hive
Cons of ODI
- Requires expertise in Java programming
- It does not support real-time integration of data
4. Snowflake Data Warehouse
Snowflake is a data analytics service provider that is cloud-based. With it, the customers can shift to cloud-based solutions.
Main Features of SDW
- It is possible to establish as many virtual warehouses as customers want
- Provides high scalability
- Integrates semi-structured and structured data without getting help from other tools
Pros of SDW
- Highly secure
- Compatible with HIPAA, SOC 1 and 2, GDPR, and PCI DSS
- Query optimization is automatic
Cons of SDW
- No restrictions on data but a user can go through huge expenses if excessive data is consumed
- Not a product of Google, Amazon, and Microsoft, so lacks some features
5. Tableau
Tableau is a popular ETL tool in the industry of big data. It collects data from various sources and helps data engineers to generate visualization and data reports.
Core Features of Tableau
- Large datasets are easily manageable
- Data visualization is possible without performance and speed issues
- Supports multiple scripting languages
Pros of Tableau
- Most efficient
- Data visualization takes less time to generate
- Provides a good user experience
- Any non-technical person can also use it
Cons on Tableau
- Automatic scheduling is not possible
- Highly expensive tool
6. Power BI
Power BI is a leading ETL tool in data engineering. It processes data, generate visualization on the dashboard, and provides insights for analysis.
Main features of Power BI
- It is affordable and cost-effective
- A basic and free desktop version is available
- Simple to use with tools that are user friendly
Pros of Power BI
- Connect with multiple sources of data like Excel, XML, JSON, etc.
- Collect data from BI tools
- More flexible
- Users can access it in offline mode also
Cons of Power BI
- Provides a limited number of options
- Restrictions on data handling
7. Pentaho
Pentaho provides data reports, data mining, and information dashboards. It is an open-source ETL software tool. It has two editions: a community edition and an enterprise edition.
Key features of Pentaho
- It can handle whether unstructured or structured data
- It can only host on Apple and Intel servers
- Offers self-paced and online learning
- The community edition is free of cost
Pros of Pentaho
- Business users can use it also
- Suitable for small and medium-sized enterprises
- Users do not need an IT knowledge
Cons of Pentaho
- No unified design interfaces
- Provides limited components
- Poor support from the community
8. Amazon Redshift
Amazon Redshift is famous for insights generation and collecting datasets. It can process a large amount of data in different warehouses and allows parallel processing.
Core Features of Amazon Redshift
- Supports massive workloads of data
- Parallel processing
- Faster due to column-based databases
Pros of Amazon Redshift
- Deployment is easy
- A simplest ETL tool
- Automation in administrative queries
- It can integrate with third-party tools
Cons of Amazon Redshift
- Supports parallel processing only
- Indexing is not unique
9. Azure Data Factory
ADF manages the integration of data at scale. It is a fully-managed and serverless tool. It is used in operational data integration, data analytics, and workflows of data engineering.
Key Features of ADF
- Highly efficient due to the quick transfer of a large amount of data
- Highly scalable due to time-slicing and parallelism
- Easy cloud migration
Pros of Azure Data Factory
- Easily understandable
- Cost-effectiveness
Cons of Azure Data Factory
- Integration has some limitations
- Not ideal in multi-cloud scenarios
- The pricing plan is complex
10. Azure Databricks
Azure Databricks is popular in data engineering and Spark-based analytics machine. It supports data engineers, data scientists, and data analysts in the modern analysis of data.
Main Features of Azure Databricks
- Provides a shared and interactive workplace
- Supports data engineers to collaborate in real-time programming
- Provides many built-in functionalities like autoscaling options.
Pros of AD
- Cost-effectiveness
- Performance effectiveness
- Interactive and simple
Cons of AD
- Inadequate documentation
- Graphs and dashboard are not as per the standard
- Visualization needs to be upgraded
Conclusion
In information technology, data engineering is throwing preserving challenges. ETL tools are the best way to transfer data from one system to another or from one data repository to another.
There are various forms of ETL tools that an enterprise can use in system and data integration. Based on the requirements, size of the enterprise, and specific budget, an enterprise can choose the best platform as per its needs.
