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Dask
Dask

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Apache Spark

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What is Dask?

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

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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Why do developers choose Dask?
Why do developers choose Apache Spark?
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      What are some alternatives to Dask and Apache Spark?
      Pandas
      Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
      PySpark
      It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
      NumPy
      Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
      Anaconda
      A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
      SciPy
      Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
      See all alternatives
      Decisions about Dask and Apache Spark
      StackShare Editors
      StackShare Editors
      Hadoop
      Hadoop
      Apache Spark
      Apache Spark
      Presto
      Presto

      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
      Hadoop
      Hadoop
      Apache Spark
      Apache Spark
      Presto
      Presto

      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
      Kafka
      Kafka
      MySQL
      MySQL
      Scala
      Scala
      Apache Spark
      Apache Spark
      Presto
      Presto

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

      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 · 333K views
      atStitch FixStitch Fix
      Kafka
      Kafka
      PostgreSQL
      PostgreSQL
      Amazon S3
      Amazon S3
      Apache Spark
      Apache Spark
      Presto
      Presto
      Python
      Python
      R
      R
      PyTorch
      PyTorch
      Docker
      Docker
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      #AWS
      #Etl
      #ML
      #DataScience
      #DataStack
      #Data

      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
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      Reviews of Dask and Apache Spark
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      How developers use Dask 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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