Why is ETL Data Integration Important?
Every organization wants its employees to make more informed data-driven choices. To better identify where they can give better onboarding and documentation, the customer support teams looks for patterns in support requests or do text analysis on interactions. The Marketing teams wants a clear picture of their ad effectiveness across platforms and the return on their investment.
They also assist them in better focusing of their resources, products and engineering teams, to look at their productivity data or the defect reports.
The mentioned teams may use the ETL process to gather the information they need for better understanding of their duties. The ETL process, which stands for Extract, Transform and Load, allows businesses to combine data from a variety of sources. The data is then ready for analysis and usage by many teams that need it, besides complex analysis, application embedding, and other data monetization activities.
What is AWS Glue?
AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning and application development. AWS Glue provides all the capabilities needed for data integration so that you can start analysing your data and put it to use in minutes instead of months.
Data Integration involves multiple tasks such as, discovering and extracting data from various sources; enriching, cleaning, normalizing and combining data; loading and organizing data in databases, data warehouses and data lakes. These tasks are often handled by different types of users where each use different products. Glue is equally capable of supporting structured and semi-structured data. Dynamic Frame is a better data frame for handling ETL workloads which generally contain messy data.
AWS Glue provides both visual and code-based interfaces to make data integration easier. Users can easily find and access data using the AWS Glue Data Catalog. Data engineers and ETL developers can visually create, run, and monitor ETL workflows with a few clicks in AWS Glue Studio. Data analysts and data scientists can use AWS Glue DataBrew to visually enrich, clean, and normalize data without writing code.
The standard three stages of AWS Glue Works are
- the AWS Glue Data Catalog(Crawlers, Database connections and Streaming Workloads),
- the Glue Jobs(ETL Jobs) and
- the Scheduler(Cron Expression and Quartz Schedules).
AWS Glue Console is a single pane of glass dashboard which retrieves the data from source, manages all information about the data, transforms the data and loads it to the destination. AWS Glue can be accessing the data and interacting with external components/systems using AWS Glue API. To Debug and Test the Python/Scala Code, the AWS Glue has a feature called Interactive sessions that supports Jupyter, Zeppelin and Sagemaker Notebooks. AWS Glue Studio is a graphical representation of the Jobs and Triggers.
What is Talend?
Talend is a data integration platform that is free and an open source. It offers data integration, data management, corporate enterprise applications, data quality, cloud storage, and Big Data software and services. Talend was the first major open-source data integration software firm to enter the market in 2005. Since then, it has developed a diverse selection of items that have received widespread acclaim.
Talend enables businesses to make choices in real-time to become more data-driven where, the data becomes more accessible, the quality improves, and can be transported swiftly to the targeted systems.
|Data Integration||AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS Glue provides both visual and code-based interfaces to make data integration easier.||Talend is a data integration solution for data integration, ETL, and Big Data. But it doesn’t offer serverless for Big Data Computation|
|User Interface||The Graphical user interface is welcoming, simple and efficient to use, fast with an intuitive and user-friendly design along with debugging capabilities.||It's simple to use and the implementation is straightforward.|
|Server/Serverless||Glue is a serverless, you won't have to deal with any of the drawbacks that come with hosting your own. Your business will not need a data centre or a server to be present on the premises.||Its server-based architecture enables combining data from many sources and it has a standalone open-source and enterprise edition.|
|Database Integration||AWS Glue can be connected with various sources such as Cloud Databases, External Databases and Streaming Sources and is adaptable to integrating with the different sources simultaneously.||Integrations with databases and online services are made easier with the pre-built widgets.|
|Crawlers / Data Catalog||AWS Glue Crawler allows users to automatically infer meta-data of data and store it in central meta-data store called Glue Data Catalog. These meta data can be easily accessed by other AWS services and third-party services using AWS Glue Data Catalog.||The solution enables users to easily create data connections without requiring extensive customized code.|
|Workloads||As it is serverless, it can handle Gigabytes and Terabytes volume of data. To handle more files, AWS Glue, provides the option to read input files in larger groups per Spark task for each AWS Glue worker.||It works well for minor loads, but it may stall when dealing with huge volumes of data.|
|Custom Code||It is possible to write custom transformations that allows to perform the complicated transformations of the data. Custom Transformation language supports for Python and Scala. JAVA and R packages can be parsed as External Libraries.||The tasks are visual, enabling the team to observe the data flow without having to go through the produced Java code. Supports for customized code in Java.|
|Inbuilt Functions||AWS glue inbuilt transformations can seamlessly integrate and enrich audit raw data saving a lot of development time.||Data from various sources may be incorporated into the data lake by providing a mechanism to map source data types to target data types for various database mixes. This enables centralization of all source data and database views to explore and develop new data sets, thus automating the data set creation process.|
|CDC||AWS Glue does not support CDC directly but can achieve it by passing an Audit Column to extract data changes.||It is simple to design task flows, and it offers high-quality data integration services which are especially useful for Enterprise application integration.|
|Schema Registry / Schema Recognition||AWS Glue Crawler has many built-in Classifiers which read the data in a data store based on the format of the data that discovers the schema automatically. AWS Glue Schema Registry is a new feature that allows you to centrally discover, control, and evolve data stream schemas.||Talend has a metadata option in the repository pane of the Talend studio which can be used to retrieve a schema as per the table schemas.|
|Data Resiliency||AWS Glue offers several features to support your Job resiliency and backup needs where AWS Global infrastructure helps on Instance Failover.
|You can set up a cluster in your Talend system to provide high availability and failover features for task execution scheduling in the Talend Administration Centre. You do this by deploying multiple Job Conductors and Job execution servers on different machines|
|Monitoring||AWS Glue with Cloudwatch Metrics for the job run makes it easier to monitor and alert on failure/success. Also, Glue Studio has a Job Monitoring Dashboard which provides an overall summary of the Job runs, and status of the job.
|Talend Activity Monitoring Console provides detailed monitoring capabilities that can be used to consolidate the collected log information, and understand the underlying component and the Job interaction, prevent faults that could be unexpectedly generated and support system management decisions.|
|Development Tools||AWS Glue supports development endpoint integration such as Sagemaker notebook, Jupyter Notebook, Pycharm Professional etc., which eases on development. Also, Glue studio supports Interactive sessions with Jupyter, Sagemaker Notebooks for Windows, Linux and Mac.||Talend ESB supports the creation of SOAP and REST web services, with full WS-*functionality, including support for WS- Addressing, WS-Reliable Messaging, and WS-Security over both HTTP and JMS transports|
|Scaling||AWS Glue can be scaled up manually and automatically to handle the huge size of big data based on workers type and count (No. of DPUs)||Talend provides a number of different strategies for Parameterization based on Context Variables. These approaches are addressed in the context of re-use with links to the technical details. These approaches are then applied to orchestrating multiple jobs at scale with dynamic control|
|Performance tuning||AWS Glue can be optimized and tune the performance by implementing Horizontal Scaling and Workload Partitioning.||Talend jobs can be optimized by enabling parallel process and partition load.|
The lack of a single source of truth, quality data and ad hoc manual reporting processes undermined top management’s visibility of integrated insights on sales, sales rep interactions, marketing reach, brand performance, market share, and territory management. Understandably, the client wanted to align information that has hitherto been in silos, to gain a 360-degree product movement view, to optimize sales planning and gain competitive edge.