AWS Data Pipeline | Aws Data Pipeline Alternatives Overview 2022 (2022)

AWS Data Pipeline | Aws Data Pipeline Alternatives Overview 2022 (1)

AWS Data Pipeline is a web service that enables regular, dependable data processing and movement between various AWS computing, storage, and on-premises data sources. With AWS Data Pipeline, you can quickly transfer the processed data to AWS services like Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR while maintaining regular access to your data wherever it is kept.

In this blog, we will discuss Data Pipeline AWS:

Overview | Need for Data Pipeline AWS| Benefits | Components of AWS Data Pipeline | Pros and Cons | Alternatives of AWS Data Pipeline | Pricing | FAQ’S


With the aid of AWS Data Pipelines, you can simply develop fault-tolerant, repeatable, and highly available complicated data processing workloads. You don’t need to be concerned about securing resource availability, controlling inter-task dependencies, retrying temporary failures or timeouts in individual tasks, or developing a failure notification system.

A data pipeline is a process of transferring raw data from many sources to a location where it will be stored and analyzed. It consists of a number of connected data processing units whose outputs are inputs to one another in a series.
AWS Data Pipeline | Aws Data Pipeline Alternatives Overview 2022 (2)

(Video) AWS Data Pipeline Tutorial | Implementing Data Pipelines in AWS | Intellipaat

Need for AWS Data Pipeline

Data storage, management, migration, and processing are all becoming more difficult and time-consuming than they were. Due to the factors outlined below, handling the data is becoming more difficult:

  • There is a large amount of created data that is unprocessed or in raw form.
  • Different types of data—the data being produced is unstructured. Converting the data to appropriate formats is a time-consuming process.
  • There are numerous data storage solutions. These include data warehouses and cloud storage solutions like Amazon S3 and Amazon Relational Database Service (RDS).
  • There are numerous ways to store data. Businesses have their own data warehouse, EC2 instance-based database servers, and cloud-based storage like Amazon S3 and Amazon Relational Database Service (RDS).
  • Managing the majority of data is time-consuming and expensive. It will be very expensive to change, store, and process data.
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Benefits of AWS Data Pipeline

  1. Managing the majority of data is time-consuming and expensive. It will be very expensive to change, store, and process data.
  2. For the fault-tolerant operation of your operations, Data Pipeline AWS is built on a distributed, highly available architecture.
  3. The AWS pipeline is very scalable due to its flexibility. It makes processing a million files, in serial or parallel, as simple as processing one file.
  4. It provides a variety of features such as scheduling, dependency tracking, and error handling.
  5. It has cheap monthly fees and is inexpensive to use.
  6. Provides total control over the computing resources used to carry out your data pipeline logic.
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Components of Data Pipeline AWS

  • Source: Alocation where a pipeline extracts information (from RDBMS, CRMs, ERPs, social media management tools, or IoT sensors).
  • Destination: The moment at which the pipeline outputs all of the extracted data. A data lake or data warehouse are two possible destinations. Data can, for instance, be immediately loaded into data visualization tools.
  • Data Flow: Data flow is the term used to describe how data moves from one location to another while undergoing modifications. ETL, or extract, transform, and load, is one of the most used data flow techniques.
  • Workflow: Workflow entails the ordering of jobs and their interdependence in a pipeline. When it runs depends on dependencies and sequencing.
  • Monitoring: Constant monitoring is essential for ensuring data accuracy, speed, data loss, and efficiency. These checks and monitoring become increasingly important as data size increases.
  • Data nodes: A data node in AWS Pipeline specifies the location and type of data used as input or output by a pipeline activity. It supports the four data node types listed below:
      • SqlDataNode: A SQL database and table query that serves as the data representation for a pipeline operation.
      • DynamoDBDataNode: A table with data that EmrActivity or HiveActivity can use.
      • RedshiftDataNode: A Redshift table containing information that RedshiftCopyActivity can use.
      • S3DataNode: An Amazon S3 location with files for pipeline activities to use.
  • Pipeline: It contains the below:
    • Pipeline Components are the means by which data pipelines interact with AWS resources.
    • When all components are compiled, an instance is produced to carry out a particular function.
    • The data pipeline AWS has a feature called “attempts” that tries retrying an operation when it fails.
  • Task runner: As its name implies, extracts tasks for execution from the data flow. When the task is finished, the status is updated. If the task is finished, the procedure finishes; if it fails, the task runner checks an attempt to retry the task and then repeats the process until all outstanding tasks have been completed.
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Pros & Cons


  • Supports the majority of AWS databases with, an easy-to-use control interface with predefined templates.
  • To only create resources and clusters when necessary.
  • The ability to schedule jobs just during certain times.
  • Protection for data both at rest and while in motion. AWS’s access control mechanism allows fine-grained control over who can use what.
  • Users are relieved of all tasks relating to system stability and recovery thanks to fault-tolerant architecture.


  • It is built for AWS services, or the AWS world, and hence works well with all of the AWS parts. If you require data from many outside services, Pipeline Data aws is not the best choice.
  • When managing numerous installations and configurations on the compute resources while working with data pipelines, might become daunting.
  • To a beginner, the data pipeline’s way of representing preconditions and branching logic may appear complex, and to be honest, there are other tools available that help to accomplish complex chains more easily. A framework like Airflow is an example.

AWS Data Pipeline Alternatives

1) Hevo- To a beginner, the data pipeline’s way of representing preconditions and branching logic may appear complex, and to be honest, there are other tools available that help to accomplish complex chains more easily.
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2) AWS Glue- AWS Glue is a fully managed, and Load (ETL) service that makes categorizing your data, cleaning, enriching, and moving it between reliably disparate data stores and data streams simple and cost-effective.
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3) Apache Airflow- Apache Airflow is a workflow engine that simplifies the planning and execution of complex data pipelines. This ensures that each job in the data pipeline is executed in the correct order and receives the resources it requires.
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4) Apache NiFi- Apache Nifi is open-source software that automates and manages data flow between source and destination. Creates, monitors, and manages data flow through a web interface.
AWS Data Pipeline | Aws Data Pipeline Alternatives Overview 2022 (9)

Pricing of AWS Data Pipelines

  • The pricing is based on the frequency with which activities and preconditions are configured in the console. In the case of actions that are executed up to once per day, AWS classifies the frequency of executions as low.
  • High-frequency activities are those that are performed more than once per day. The low-frequency one on AWS costs $.6 per month, while the one on on-premise systems costs $1.5 per month. For on-premise systems, high-frequency activities start at $1 per month and go up to $2.5 per month.
  • All resources used in pipeline activity, such as EC2 instances, EMR clusters, Redshift databases, and so on, are charged at standard rates, which are in addition to the pipeline pricing. The charges mentioned above are only for pipeline features.

Frequently Asked Questions:

Q1: How is Data Pipeline AWS different from Amazon Simple Workflow Service?
Ans- It is specifically created to facilitate the exact stages that are common across the bulk of data-driven workflows, even though both services include execution tracking, resolving retries and exceptions, and performing arbitrary actions. Examples include scheduling chained transforms, quickly copying data across multiple data stores, and starting actions only when their incoming data satisfies certain readiness criteria.

(Video) Build a Big Data Analytics Pipeline Using Modern Data Architecture | Amazon Web Services

Q2: Does Data Pipeline supply any standard Activities?
Ans- Yes, Data Pipeline Aws provides built-in support for the following activities:

  • CopyActivity: This activity can conduct a SQL query and copy the results into Amazon S3 or copy data between Amazon S3 and JDBC data sources.
  • HiveActivity: With the help of this activity, running Hive queries are simple.
  • EMRActivity: With this activity, you can execute any Amazon EMR operation.
    You may run any Linux shell command or program using the ShellCommandActivity.

Q3: Can you define multiple schedules for different activities in the same pipeline?
Ans- Yes, just create numerous schedule objects in your pipeline design file and use the schedule field to link the selected schedule to the appropriate activity. This enables you to create a pipeline where, for instance, hourly log data are saved in Amazon S3 and used to generate an aggregate report once every day.

Q4: How can I get started with Data Pipeline Aws?
Ans- To begin using Data Pipelines, go to the AWS Management Console and select the Data Pipeline tab. You can then use a simple graphical editor to create a pipeline.

Q5: How many pipelines can I create in Data Pipeline aws?
Ans- By default, your account can have 100 pipelines.

Q6: Is AWS data pipeline an ETL tool?

(Video) Rudolf Eremyan - Building Data Pipelines on AWS | PyData Yerevan 2022

Ans- It is an ETL service that allows you to automate data movement and transformation. It starts an Amazon EMR cluster at each scheduled interval, submits jobs as steps to the cluster, and shuts down the cluster when tasks are completed.

Q7. Is AWS data pipeline serverless?

Ans- AWS Glue and AWS Step Functions provide serverless components to build, orchestrate, and run pipelinesthat can easily scale to process large data volumes.

Q8. What is AWS data pipeline vs. AWS glue?

Ans. With AWS Glue, you pay an hourly rate, billed by the second, for crawlers and ETL jobs. Data Pipeline Aws is billed based on how often your activities and preconditions are scheduled to run and where they run (AWS or on-premises). AWS Glue runs ETL jobs on its virtual resources in a serverless Apache Spark environment. Data Pipeline Aws isn’t limited to Apache Spark.It enables you to use other engines like Hive or Pig. Thus, if your ETL jobs don’t require the use of Apache Spark or multiple engines, AWS Data Pipelines might be preferable.

(Video) 5 best data migration tools for 2022 | Cloud Analogy

Related Links/References

  • AWS Certified Solutions Architect Associate SAA-C03 Exam details
  • AWS Free Tier: Create an Account
  • AWS Free Tier Limits
  • AWS Free Tier Account Details
  • AWS Shield | DDoS Attacks | AWS Shield Pricing: Overview
  • AWS Virtual Private Network (AWS VPN): Everything You need to Know
  • AWS Free Tier Account Services
  • AWS Data Pipeline
  • AWS Data Exchange
  • AWS Timestream
  • Amazon EMR
  • Amazon Detective

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What is the difference between AWS Glue and AWS data pipeline? ›

AWS Glue provides support for Amazon S3, Amazon RDS, Redshift, SQL, and DynamoDB and also provides built-in transformations. On the other hand, AWS Data Pipeline allows you to create data transformations through APIs and also through JSON, while only providing support for DynamoDB, SQL, and Redshift.

Is AWS data pipeline an ETL tool? ›

AWS Data Pipeline Product Details

As a managed ETL (Extract-Transform-Load) service, AWS Data Pipeline allows you to define data movement and transformations across various AWS services, as well as for on-premises resources.

Is Snowflake a data pipeline? ›

Data Pipelines in Snowflake

Modern Data Engineering with Snowflake's platform allows you to use data pipelines for data ingestion into your data lake or data warehouse. Data pipelines in Snowflake can be batch or continuous, and processing can happen directly within Snowflake itself.

Is AWS Glue good for ETL? ›

AWS Glue can run your extract, transform, and load (ETL) jobs as new data arrives. For example, you can configure AWS Glue to initiate your ETL jobs to run as soon as new data becomes available in Amazon Simple Storage Service (S3).

What is the difference between ETL and pipeline? ›

How ETL and Data Pipelines Relate. ETL refers to a set of processes extracting data from one system, transforming it, and loading it into a target system. A data pipeline is a more generic term; it refers to any set of processing that moves data from one system to another and may or may not transform it.

Is AWS CodePipeline similar to Jenkins? ›

Jenkins and AWS CodePipeline are both easy to use and set up. Jenkins installation is straightforward and can be completed in minutes. AWS provides templates that rely on CodeBuild and CodeDeploy to start creating your pipelines.

Is lambda a ETL? ›

AWS Lambda is the platform where we do the programming to perform ETL, but AWS lambda doesn't include most packages/Libraries which are used on a daily basis (Pandas, Requests) and the standard pip install pandas won't work inside AWS lambda.

Which ETL tool is used most? ›

Talend Open Studio.

Talend's ETL tool is the most popular open source ETL product. Open Studio generates Java code for ETL pipelines, rather than running pipeline configurations through an ETL engine. This approach gives it some performance advantages.

What is a modern data pipeline? ›

Modern data pipelines make it faster and easier to extract information from the data you collect. They start with extracting raw data from a number of different sources. The data is then collected and transported to a common place, typically a data repository in the cloud.

Can Snowflake compete with AWS? ›

Snowflake started as an AWS customer and competitor. While today it also runs on Microsoft Azure and Google Cloud Platform, Snowflake was born on AWS and a “substantial majority” of its business operates on AWS' public cloud infrastructure, according to regulatory filings.

Can Snowflake be called an ETL? ›

Snowflake ETL means applying the process of ETL to load data into the Snowflake Data Warehouse. This comprises the extraction of relevant data from Data Sources, making necessary transformations to make the data analysis-ready, and then loading it into Snowflake.

Is Snowflake owned by AWS? ›

Snowflake is an AWS Partner offering software solutions and has achieved Data Analytics, Machine Learning, and Retail Competencies.

When should you not use AWS Glue? ›

7 Limitations that come with AWS Glue
  1. Amount of Work Involved in the Customization.
  2. Integration with other Platforms.
  3. Limitations of Real-time data.
  4. Required Skillset.
  5. Database Support Limitations.
  6. Process Speed and Room for Flexibility.
  7. Lack of Available Use Cases and Documentation.
14 Aug 2020

What is AWS Glue vs Lambda? ›

AWS Glue is the fully managed ETL service and AWS Lambda is event-driven serverless computing platform of AWS. With AWS Glue you can crawl the metadata of unstructured data, explore the data schema, have your data catalogue as a table ,view the data on AWS Athena(SQL Query Engine)…

Can AWS Glue run SQL query? ›

The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog. You can configure your AWS Glue jobs and development endpoints to use the Data Catalog as an external Apache Hive metastore. You can then directly run Apache Spark SQL queries against the tables stored in the Data Catalog.

Is ETL outdated? ›

Quite simply, it is outdated because it predated cloud storage solutions. When building an ETL pipeline data analysts and data engineers normally follow a certain workflow that includes the following steps. Let's take a look at them and see if we can spot the problems.

Is ETL have future? ›

There are multiple companies to hire these skills. In this way, we can say that this popular ETL software will have a good demand in the future IT market.

Can Kafka do ETL? ›

Kafka is a great choice for doing ETL (extract-transform-load): Connect provides the E and the L, Streams the T, and Kafka itself provides a storage buffer that provides scalability, resiliency, and replayability of the process.

Is CodePipeline a CI or CD? ›

The CI/CD pipeline is built using AWS CodePipeline , and utilizes a continuous delivery service that models, visualizes, and automates the steps required to release software.

Is CodePipeline better than Jenkins? ›

Since CodePipeline is a managed service, you can run many pipelines in parallel without having to deal with additional infrastructure. Jenkins on the other hand is limited by the number of executors you have.

What are the 3 types of pipelines in Jenkins? ›

Different Types of Jenkins CI/CD Pipelines. Scripted Pipeline. Declarative Pipeline.

What ETL does Amazon use? ›

AWS Glue is the ETL tool offered by Amazon Web Services. Glue is a serverless platform and toolset that can extract data from various sources, transform it in different ways (enrich, cleanse, combine, and normalize), and load and organize data in destination databases, data warehouses, and data lakes.

Is Lambda deprecated? ›

AWS are deprecating Node. js 12 for AWS Lambda in two stages: Starting November 14, 2022, Lambda will no longer apply security patches and other updates to the Node. js 12 runtime used by Lambda functions, and functions using Node.

Is Elasticsearch an ETL? ›

No, Elasticsearch is not an ETL tool. It is a free and open-source search engine for text, numeric, geospatial, structured, and unstructured data. Elasticsearch is mostly used in business intelligence, security intelligence, and operational intelligence. There are separate ETL tools available for Elasticsearch.

Which ETL tool is in demand in 2022? ›

Here are the five most popular ETL tools in 2022:

Talend Data Fabric. Informatica PowerCenter. Fivetran. Stitch.

What is the latest ETL tool? ›

15 Best ETL Tools In 2022
  1. Informatica PowerCenter - Cloud data management solution. ...
  2. Microsoft SQL Server Integration Services - Enterprise ETL platform. ...
  3. Talend Data Fabric - Enterprise data integration with open-source ETL tool. ...
  4. (XPlenty) - ETL tool for e-commerce. ...
  5. Stitch - Modern, managed ETL service.
17 Aug 2022

Which ETL tool is easiest? ›

Which ETL tool is easiest? It depends from user to user but some of the easiest ETL Tools that you can learn are Hevo, Dataddo, Talend, Apache Nifi because of their simple-to-understand UI and as they don't require too much technical knowledge.

Is DBT an ETL tool? ›

dbt is not an ETL tool.

What is the difference between data flow and pipeline? ›

Data moves from one component to the next via a series of pipes. Data flows through each pipe from left to right. A "pipeline" is a series of pipes that connect components together so they form a protocol.

Which platform is best for big data? ›

Most Popular Big Data Platforms
  • 1010 Data. 1010 Data is a scalable cloud-native end-to-end platform to handle big data acquisition, self-service data management and advanced analytics with AI and machine learning capabilities. ...
  • Cloudera. ...
  • Pivotal. ...
  • Microsoft Azure HDInsight. ...
13 Oct 2022

What are 3 important stages in pipeline? ›

The following stages make up the pipeline: the Fetch stage. the Decode stage. the Execute stage.

What are the two types of pipelines? ›

There are two types of oil pipeline: crude oil pipeline and product pipeline.

What are the 2 types of pipeline installation? ›

There are two main categories of pipelines used to transport energy products: petroleum pipelines and natural gas pipelines.

Is Kafka a data pipeline? ›

Organizations will be able to collect data from a number of sources and store it in a single location thanks to ODS. Kafka is a distributed data storage that may be used to create real-time data pipelines.

What is DBT data pipeline? ›

dbt (data build tool) automatically generates documentation around descriptions, models dependencies, model SQL, sources, and tests. dbt creates lineage graphs of the data pipeline, providing transparency and visibility into what the data is describing, how it was produced, as well as how it maps to business logic.

What is NLP data pipeline? ›

The set of ordered stages one should go through from a labeled dataset to creating a classifier that can be applied to new samples is called the NLP pipeline.

What is data pipeline in AWS? ›

AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals.

What is difference between AWS Glue and lambda? ›

Glue can only execute jobs using Scala or Python code. Lambda can execute code from triggers by other services (SQS, Kafka, DynamoDB, Kinesis, CloudWatch, etc.) vs. Glue which can be triggered by lambda events, another Glue jobs, manually or from a schedule.

Is Glue an ETL tool? ›

AWS Glue is simply a serverless ETL tool. ETL refers to three (3) processes that are commonly needed in most Data Analytics / Machine Learning processes: Extraction, Transformation, Loading. Extracting data from a source, transforming it in the right way for applications, and then loading it back to the data warehouse.

What is the difference between EMR and Glue? ›

Amazon EMR has a much richer feature set, including Hadoop component hosting compatibility, TensorFlow machine learning libraries, and Presto SQL queries. Glue is suited to simpler data ETL and integration workflows, whereas EMR is a more comprehensive data operations managed service platform.

Can Lambda be used for ETL? ›

However, sometimes, you do not need to process vast amounts of data for your smaller projects. Instead, you can use micro ETL with the help of AWS Lambda to get relevant data immediately. With AWS Lambda functions, you can trigger time-based events to extract, transform, and save the data into a central repository.

Is AWS Lambda an ETL tool? ›

The ETL (extract, transform, and load) pipeline was created using AWS Lambda functions based on Python/Pandas. The pipeline was designed to execute using a series of Amazon S3 buckets and to return the results, logs, and errors to Amazon API Gateway.

Is AWS Glue just spark? ›

AWS Glue runs your ETL jobs in an Apache Spark serverless environment. AWS Glue runs these jobs on virtual resources that it provisions and manages in its own service account.

Which ETL tool is best? ›

8 More Top ETL Tools to Consider
  • 1) Striim. Striim offers a real-time data integration platform for big data workloads. ...
  • 2) Matillion. Matillion is a cloud ETL platform that can integrate data with Redshift, Snowflake, BigQuery, and Azure Synapse. ...
  • 3) Pentaho. ...
  • 4) AWS Glue. ...
  • 5) Panoply. ...
  • 6) Alooma. ...
  • 7) Hevo Data. ...
  • 8) FlyData.

What is AWS Glue vs Athena? ›

A key difference between Glue and Athena is that Athena is primarily used as a query tool for analytics and Glue is more of a transformation and data movement tool. Creating tables for Glue to use in ETL jobs. The table must have a property added to them called a classification, which identifies the format of the data.

Which is cheaper glue or EMR? ›

But, on the other hand, Amazon EMR is less costly as you already have the required setup. Flexible – AWS Glue is a flexible and easily scalable ETL platform as it works on AWS serverless platform. Better – AWS Glue is a flexible and easily scalable ETL platform as it works on AWS serverless platform.

Is AWS Glue using EMR? ›

The AWS Glue Data Catalog provides a unified metadata repository across a variety of data sources and data formats, integrating with Amazon EMR as well as Amazon RDS, Amazon Redshift, Redshift Spectrum, Athena, and any application compatible with the Apache Hive metastore.


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