What is Apache Beam and what are its top alternatives?
Apache Beam is an open-source unified programming model that allows you to define batch and streaming data processing jobs and run them seamlessly across different execution engines such as Apache Flink, Apache Spark, and Google Cloud Dataflow. It provides a flexible and portable way to create data processing pipelines that can scale from a single node to large clusters. However, Beam can be complex to learn and lacks out-of-the-box support for some advanced features found in other frameworks.
- Apache Flink: Apache Flink is a powerful open-source stream processing framework with support for both batch and stream processing. It offers low latency and high throughput, fault tolerance, and exactly-once processing semantics. Pros: Powerful stream processing capabilities, excellent performance. Cons: Steeper learning curve compared to Apache Beam.
- Apache Spark: Apache Spark is another popular open-source distributed processing framework that supports batch and stream processing. It provides rich APIs in multiple languages, built-in libraries for machine learning and graph processing, and can run on various cluster managers. Pros: Versatile, extensive ecosystem. Cons: Does not provide native support for some stream processing functionalities.
- Google Cloud Dataflow: Google Cloud Dataflow is a fully managed stream and batch processing service based on Apache Beam. It offers auto-scaling, built-in monitoring and debugging tools, and seamless integration with other Google Cloud services. Pros: Fully managed service, easy to deploy. Cons: Limited to Google Cloud Platform.
- Apache Kafka Streams: Apache Kafka Streams is a lightweight stream processing library that is tightly integrated with Apache Kafka, a distributed streaming platform. It allows for building real-time applications and microservices without the need for external processing tools. Pros: Seamless integration with Kafka, lightweight. Cons: Limited functionality compared to full-fledged processing frameworks.
- StreamSets Data Collector: StreamSets Data Collector is an open-source platform for designing, executing, and monitoring data pipelines. It offers a visual interface for building pipelines, support for various sources and destinations, and built-in validation and monitoring features. Pros: Intuitive visual interface, extensive connectivity. Cons: Less focus on advanced stream processing capabilities.
- Confluent Platform: Confluent Platform is a complete event streaming platform built on Apache Kafka that includes additional components for data integration, real-time analytics, and data governance. It provides enterprise-grade features and commercial support for running Kafka-based stream processing applications. Pros: Enterprise-grade features, commercial support. Cons: Cost associated with enterprise features.
- AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service that can also perform stream processing using Apache Spark. It offers data cataloging, job scheduling, and serverless execution of data transformation tasks. Pros: Serverless execution, seamless integration with other AWS services. Cons: Limited to the AWS ecosystem.
- Spring Cloud Data Flow: Spring Cloud Data Flow is a toolkit for building data integration and stream processing applications based on the popular Spring Boot framework. It offers a web-based dashboard for managing data pipelines, support for multiple runtime platforms, and integration with Spring Cloud Stream and Spring Cloud Task. Pros: Integration with Spring ecosystem, flexible deployment options. Cons: Relatively young project compared to established frameworks.
- Databricks: Databricks is a unified data analytics platform built on Apache Spark that provides collaborative workspace, interactive notebooks, and optimized performance for big data processing. It offers automated cluster management, built-in machine learning libraries, and integration with various data sources. Pros: Usability, built-in machine learning capabilities. Cons: Cost associated with the platform.
- Presto: Presto is a distributed SQL query engine optimized for interactive analytics on large datasets. It can connect to multiple data sources, including Hadoop, MySQL, and Kafka, and allows for querying data across different storage systems with high performance. Pros: High performance, SQL compatibility. Cons: Not a dedicated stream processing framework.
Top Alternatives to Apache Beam
- Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...
- Kafka Streams
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology. ...
- Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...
- Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...
- Google Cloud Dataflow
Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization. ...
- Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala. ...
- AWS Glue
A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. ...
- StreamSets
An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps. ...
Apache Beam alternatives & related posts
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
- Speed4
related Apache Spark posts
The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.
Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).
At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.
For more info:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
As a frontend engineer on the Algorithms & Analytics team at Stitch Fix, I work with data scientists to develop applications and visualizations to help our internal business partners make data-driven decisions. I envisioned a platform that would assist data scientists in the data exploration process, allowing them to visually explore and rapidly iterate through their assumptions, then share their insights with others. This would align with our team's philosophy of having engineers "deploy platforms, services, abstractions, and frameworks that allow the data scientists to conceive of, develop, and deploy their ideas with autonomy", and solve the pain of data exploration.
The final product, code-named Dora, is built with React, Redux.js and Victory, backed by Elasticsearch to enable fast and iterative data exploration, and uses Apache Spark to move data from our Amazon S3 data warehouse into the Elasticsearch cluster.
related Kafka Streams posts
I have recently started using Confluent/Kafka cloud. We want to do some stream processing. As I was going through Kafka I came across Kafka Streams and KSQL. Both seem to be A good fit for stream processing. But I could not understand which one should be used and one has any advantage over another. We will be using Confluent/Kafka Managed Cloud Instance. In near future, our Producers and Consumers are running on premise and we will be interacting with Confluent Cloud.
Also, Confluent Cloud Kafka has a primitive interface; is there any better UI interface to manage Kafka Cloud Cluster?
We currently have 2 Kafka Streams topics that have records coming in continuously. We're looking into joining the 2 streams based on a key with a window of 5 minutes based on their timestamp.
Should I consider kStream - kStream join or Apache Flink window joins? Or is there any other better way to achieve this?
- High-throughput126
- Distributed119
- Scalable92
- High-Performance86
- Durable66
- Publish-Subscribe38
- Simple-to-use19
- Open source18
- Written in Scala and java. Runs on JVM12
- Message broker + Streaming system9
- KSQL4
- Avro schema integration4
- Robust4
- Suport Multiple clients3
- Extremely good parallelism constructs2
- Partioned, replayable log2
- Simple publisher / multi-subscriber model1
- Fun1
- Flexible1
- Non-Java clients are second-class citizens32
- Needs Zookeeper29
- Operational difficulties9
- Terrible Packaging5
related Kafka posts
When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?
So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.
React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.
Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.
To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.
Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.
We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.
Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.
#BigData #AWS #DataScience #DataEngineering
Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
related Airflow posts
Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.
Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”
There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.
Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.
Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.
Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.
We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.
Google Cloud Dataflow
- Unified batch and stream processing7
- Autoscaling5
- Fully managed4
- Throughput Transparency3
related Google Cloud Dataflow posts
Will Dataflow be the right replacement for AWS Glue? Are there any unforeseen exceptions like certain proprietary transformations not supported in Google Cloud Dataflow, connectors ecosystem, Data Quality & Date cleansing not supported in DataFlow. etc?
Also, how about Google Cloud Data Fusion as a replacement? In terms of No Code/Low code .. (Since basic use cases in Glue support UI, in that case, CDF may be the right choice ).
What would be the best choice?
I am currently launching 50 pipelines in a Google Cloud Data Fusion version 6.4 instance. These pipelines are launched daily and transport data from a MySQLServer database to Google BigQuery. The cost is becoming very high and I was wondering if the costs with Google Cloud Dataflow decrease for the same rows transported.
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
related Apache Flink posts
I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.
I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?
- Managed Hive Metastore9
related AWS Glue posts
Will Dataflow be the right replacement for AWS Glue? Are there any unforeseen exceptions like certain proprietary transformations not supported in Google Cloud Dataflow, connectors ecosystem, Data Quality & Date cleansing not supported in DataFlow. etc?
Also, how about Google Cloud Data Fusion as a replacement? In terms of No Code/Low code .. (Since basic use cases in Glue support UI, in that case, CDF may be the right choice ).
What would be the best choice?
Hi,
We are currently storing the data in Amazon S3 using Apache Parquet format. We are using Presto to query the data from S3 and catalog it using AWS Glue catalog. We have Metabase sitting on top of Presto, where our reports are present. Currently, Presto is becoming too costly for us, and we are looking for alternatives for it but want to use the remaining setup (S3, Metabase) as much as possible. Please suggest alternative approaches.
- No user community2
- Crashes1