Alternatives to Google Cloud Dataflow logo

Alternatives to Google Cloud Dataflow

Apache Spark, Kafka, Hadoop, Akutan, and Apache Beam are the most popular alternatives and competitors to Google Cloud Dataflow.
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What is Google Cloud Dataflow and what are its top alternatives?

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.
Google Cloud Dataflow is a tool in the Real-time Data Processing category of a tech stack.

Top Alternatives to Google Cloud Dataflow

  • Apache Spark
    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
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • Hadoop
    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Akutan
    Akutan

    A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world. ...

  • Apache Beam
    Apache Beam

    It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments. ...

  • Google Cloud Data Fusion
    Google Cloud Data Fusion

    A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more. ...

  • Airflow
    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. ...

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

Google Cloud Dataflow alternatives & related posts

Apache Spark logo

Apache Spark

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Fast and general engine for large-scale data processing
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PROS OF APACHE SPARK
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    Open-source
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    Fast and Flexible
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    One platform for every big data problem
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    Great for distributed SQL like applications
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    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
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    Machine learning libratimery, Streaming in real
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    In memory Computation
CONS OF APACHE SPARK
  • 4
    Speed

related Apache Spark posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M views

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:

#DataScience #DataStack #Data

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Patrick Sun
Software Engineer at Stitch Fix · | 10 upvotes · 56.4K views

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.

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Kafka logo

Kafka

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
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    High-throughput
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    Distributed
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    Scalable
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    High-Performance
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    Durable
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    Publish-Subscribe
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    Simple-to-use
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    Open source
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    Written in Scala and java. Runs on JVM
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    Message broker + Streaming system
  • 4
    KSQL
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    Avro schema integration
  • 4
    Robust
  • 3
    Suport Multiple clients
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    Extremely good parallelism constructs
  • 2
    Partioned, replayable log
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    Simple publisher / multi-subscriber model
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    Fun
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    Flexible
CONS OF KAFKA
  • 32
    Non-Java clients are second-class citizens
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    Needs Zookeeper
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    Operational difficulties
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    Terrible Packaging

related Kafka posts

Nick Rockwell
SVP, Engineering at Fastly · | 46 upvotes · 4.1M views

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.

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Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3.3M views

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

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Hadoop logo

Hadoop

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Open-source software for reliable, scalable, distributed computing
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PROS OF HADOOP
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    Great ecosystem
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    One stack to rule them all
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    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
CONS OF HADOOP
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    related Hadoop posts

    Shared insights
    on
    KafkaKafkaHadoopHadoop
    at

    The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

    For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

    Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 3M views

    Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

    Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

    https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

    (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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    Akutan logo

    Akutan

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    A Distributed Knowledge Graph Store
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    PROS OF AKUTAN
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      CONS OF AKUTAN
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        related Akutan posts

        Apache Beam logo

        Apache Beam

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        A unified programming model
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        PROS OF APACHE BEAM
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          Open-source
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          Cross-platform
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          Portable
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          Unified batch and stream processing
        CONS OF APACHE BEAM
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          related Apache Beam posts

          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?

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          Google Cloud Data Fusion logo

          Google Cloud Data Fusion

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          Fully managed, code-free data integration at any scale
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          PROS OF GOOGLE CLOUD DATA FUSION
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            Lower total cost of pipeline ownership
          CONS OF GOOGLE CLOUD DATA FUSION
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            related Google Cloud Data Fusion 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?

            See more

            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.

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            Airflow logo

            Airflow

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            A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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            PROS OF AIRFLOW
            • 53
              Features
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              Task Dependency Management
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              Beautiful UI
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              Cluster of workers
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              Extensibility
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              Open source
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              Complex workflows
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              Python
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              Good api
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              Apache project
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              Custom operators
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              Dashboard
            CONS OF AIRFLOW
            • 2
              Observability is not great when the DAGs exceed 250
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              Running it on kubernetes cluster relatively complex
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              Open source - provides minimum or no support
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              Logical separation of DAGs is not straight forward

            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.

            See more

            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.

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            Talend logo

            Talend

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            A single, unified suite for all integration needs
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            PROS OF TALEND
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              CONS OF TALEND
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                related Talend posts