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Apex vs Apache Spark: What are the differences?

Apex: Serverless Architecture with AWS Lambda. Apex is a small tool for deploying and managing AWS Lambda functions. With shims for languages not yet supported by Lambda, you can use Golang out of the box; Apache Spark: Fast and general engine for large-scale data processing. 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.

Apex belongs to "Serverless / Task Processing" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".

Some of the features offered by Apex are:

  • Supports languages Lambda does not natively support via shim, such as Go
  • Binary install (useful for continuous deployment in CI etc)
  • Project level function and resource management

On the other hand, Apache Spark provides the following key features:

  • Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
  • Write applications quickly in Java, Scala or Python
  • Combine SQL, streaming, and complex analytics

Apex and Apache Spark are both open source tools. Apache Spark with 22.3K GitHub stars and 19.3K forks on GitHub appears to be more popular than Apex with 7.82K GitHub stars and 567 GitHub forks.

What is Apex?

Apex is a small tool for deploying and managing AWS Lambda functions. With shims for languages not yet supported by Lambda, you can use Golang out of the box.

What is 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.
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      What are some alternatives to Apex and Apache Spark?
      AWS Lambda
      AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.
      Serverless
      Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.
      Cloud Functions for Firebase
      Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.
      Google Cloud Functions
      Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running
      Azure Functions
      Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
      See all alternatives
      Decisions about Apex and Apache Spark
      StackShare Editors
      StackShare Editors
      Presto
      Presto
      Apache Spark
      Apache Spark
      Hadoop
      Hadoop

      Around 2015, the growing use of Uber’s data exposed limitations in the ETL and Vertica-centric setup, not to mention the increasing costs. “As our company grew, scaling our data warehouse became increasingly expensive. To cut down on costs, we started deleting older, obsolete data to free up space for new data.”

      To overcome these challenges, Uber rebuilt their big data platform around Hadoop. “More specifically, we introduced a Hadoop data lake where all raw data was ingested from different online data stores only once and with no transformation during ingestion.”

      “In order for users to access data in Hadoop, we introduced Presto to enable interactive ad hoc user queries, Apache Spark to facilitate programmatic access to raw data (in both SQL and non-SQL formats), and Apache Hive to serve as the workhorse for extremely large queries.

      See more
      StackShare Editors
      StackShare Editors
      Presto
      Presto
      Apache Spark
      Apache Spark
      Hadoop
      Hadoop

      To improve platform scalability and efficiency, Uber transitioned from JSON to Parquet, and built a central schema service to manage schemas and integrate different client libraries.

      While the first generation big data platform was vulnerable to upstream data format changes, “ad hoc data ingestions jobs were replaced with a standard platform to transfer all source data in its original, nested format into the Hadoop data lake.”

      These platform changes enabled the scaling challenges Uber was facing around that time: “On a daily basis, there were tens of terabytes of new data added to our data lake, and our Big Data platform grew to over 10,000 vcores with over 100,000 running batch jobs on any given day.”

      See more
      StackShare Editors
      StackShare Editors
      Presto
      Presto
      Apache Spark
      Apache Spark
      Scala
      Scala
      MySQL
      MySQL
      Kafka
      Kafka

      Slack’s data team works to “provide an ecosystem to help people in the company quickly and easily answer questions about usage, so they can make better and data informed decisions.” To achieve that goal, that rely on a complex data pipeline.

      An in-house tool call Sqooper scrapes MySQL backups and pipe them to S3. Job queue and log data is sent to Kafka then persisted to S3 using an open source tool called Secor, which was created by Pinterest.

      For compute, Amazon’s Elastic MapReduce (EMR) creates clusters preconfigured for Presto, Hive, and Spark.

      Presto is then used for ad-hoc questions, validating data assumptions, exploring smaller datasets, and creating visualizations for some internal tools. Hive is used for larger data sets or longer time series data, and Spark allows teams to write efficient and robust batch and aggregation jobs. Most of the Spark pipeline is written in Scala.

      Thrift binds all of these engines together with a typed schema and structured data.

      Finally, the Hive Metastore serves as the ground truth for all data and its schema.

      See more
      StackShare Editors
      StackShare Editors
      Apache Thrift
      Apache Thrift
      Kotlin
      Kotlin
      Presto
      Presto
      HHVM (HipHop Virtual Machine)
      HHVM (HipHop Virtual Machine)
      gRPC
      gRPC
      Kubernetes
      Kubernetes
      Apache Spark
      Apache Spark
      Airflow
      Airflow
      Terraform
      Terraform
      Hadoop
      Hadoop
      Swift
      Swift
      Hack
      Hack
      Memcached
      Memcached
      Consul
      Consul
      Chef
      Chef
      Prometheus
      Prometheus

      Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

      Apps
      • Web: a mix of JavaScript/ES6 and React.
      • Desktop: And Electron to ship it as a desktop application.
      • Android: a mix of Java and Kotlin.
      • iOS: written in a mix of Objective C and Swift.
      Backend
      • The core application and the API written in PHP/Hack that runs on HHVM.
      • The data is stored in MySQL using Vitess.
      • Caching is done using Memcached and MCRouter.
      • The search service takes help from SolrCloud, with various Java services.
      • The messaging system uses WebSockets with many services in Java and Go.
      • Load balancing is done using HAproxy with Consul for configuration.
      • Most services talk to each other over gRPC,
      • Some Thrift and JSON-over-HTTP
      • Voice and video calling service was built in Elixir.
      Data warehouse
      • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
      Etc
      See more
      Eric Colson
      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 266.9K views
      atStitch FixStitch Fix
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      Docker
      Docker
      PyTorch
      PyTorch
      R
      R
      Python
      Python
      Presto
      Presto
      Apache Spark
      Apache Spark
      Amazon S3
      Amazon S3
      PostgreSQL
      PostgreSQL
      Kafka
      Kafka
      #Data
      #DataStack
      #DataScience
      #ML
      #Etl
      #AWS

      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

      See more
      Interest over time
      Reviews of Apex and Apache Spark
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      How developers use Apex and Apache Spark
      Avatar of Wei Chen
      Wei Chen uses Apache SparkApache Spark

      Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

      Avatar of Ralic Lo
      Ralic Lo uses Apache SparkApache Spark

      Used Spark Dataframe API on Spark-R for big data analysis.

      Avatar of Kalibrr
      Kalibrr uses Apache SparkApache Spark

      We use Apache Spark in computing our recommendations.

      Avatar of BrainFinance
      BrainFinance uses Apache SparkApache Spark

      As a part of big data machine learning stack (SMACK).

      Avatar of Dotmetrics
      Dotmetrics uses Apache SparkApache Spark

      Big data analytics and nightly transformation jobs.

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