Alternatives to Hadoop logo

Alternatives to Hadoop

Cassandra, MongoDB, Elasticsearch, Splunk, and Snowflake are the most popular alternatives and competitors to Hadoop.
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What is Hadoop and what are its top alternatives?

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.
Hadoop is a tool in the Databases category of a tech stack.
Hadoop is an open source tool with 11.7K GitHub stars and 7.2K GitHub forks. Here’s a link to Hadoop's open source repository on GitHub

Top Alternatives to Hadoop

  • Cassandra

    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

  • MongoDB

    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • Elasticsearch

    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

  • Splunk

    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Snowflake

    Snowflake

    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn. ...

  • 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. ...

  • Kubernetes

    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • HBase

    HBase

    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop. ...

Hadoop alternatives & related posts

Cassandra logo

Cassandra

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A partitioned row store. Rows are organized into tables with a required primary key.
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PROS OF CASSANDRA
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    Distributed
  • 90
    High performance
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    High availability
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    Easy scalability
  • 50
    Replication
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    Reliable
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    Multi datacenter deployments
  • 6
    Schema optional
  • 6
    OLTP
  • 5
    Open source
  • 2
    Workload separation (via MDC)
CONS OF CASSANDRA
  • 2
    Reliability of replication
  • 1
    Updates

related Cassandra posts

Thierry Schellenbach
Shared insights
on
Redis
Cassandra
RocksDB
at

1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

#InMemoryDatabases #DataStores #Databases

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Umair Iftikhar
Technical Architect at Vappar · | 3 upvotes · 68.2K views

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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MongoDB logo

MongoDB

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The database for giant ideas
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PROS OF MONGODB
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    Document-oriented storage
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    No sql
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    Ease of use
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    Fast
  • 405
    High performance
  • 253
    Free
  • 214
    Open source
  • 178
    Flexible
  • 140
    Replication & high availability
  • 108
    Easy to maintain
  • 40
    Querying
  • 36
    Easy scalability
  • 35
    Auto-sharding
  • 34
    High availability
  • 30
    Map/reduce
  • 26
    Document database
  • 24
    Easy setup
  • 24
    Full index support
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    Reliable
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    Fast in-place updates
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    Agile programming, flexible, fast
  • 11
    No database migrations
  • 7
    Enterprise
  • 7
    Easy integration with Node.Js
  • 5
    Enterprise Support
  • 4
    Great NoSQL DB
  • 3
    Aggregation Framework
  • 3
    Support for many languages through different drivers
  • 3
    Drivers support is good
  • 2
    Schemaless
  • 2
    Fast
  • 2
    Awesome
  • 2
    Managed service
  • 2
    Easy to Scale
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    Consistent
CONS OF MONGODB
  • 5
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 1
    Proprietary query language

related MongoDB posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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Elasticsearch logo

Elasticsearch

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Open Source, Distributed, RESTful Search Engine
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PROS OF ELASTICSEARCH
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    Powerful api
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    Great search engine
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    Open source
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    Restful
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    Near real-time search
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    Free
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    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Great docs
  • 3
    Awesome, great tool
  • 3
    Easy to scale
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Fast
  • 2
    Nosql DB
  • 2
    Easy setup
  • 2
    Highly Available
  • 2
    Document Store
  • 2
    Great customer support
  • 1
    Reliable
  • 1
    Not stable
  • 1
    Potato
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Scalability
  • 0
    Easy to get hot data
  • 0
    Community
CONS OF ELASTICSEARCH
  • 6
    Diffecult to get started
  • 5
    Resource hungry
  • 4
    Expensive
  • 3
    Hard to keep stable at large scale

related Elasticsearch posts

Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

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Tymoteusz Paul
Devops guy at X20X Development LTD · | 21 upvotes · 4.3M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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PROS OF SPLUNK
  • 2
    API for searching logs, running reports
  • 1
    Query engine supports joining, aggregation, stats, etc
  • 1
    Ability to style search results into reports
  • 1
    Query any log as key-value pairs
  • 1
    Splunk language supports string, date manip, math, etc
  • 1
    Granular scheduling and time window support
  • 1
    Alert system based on custom query results
  • 1
    Custom log parsing as well as automatic parsing
  • 1
    Dashboarding on any log contents
  • 1
    Rich GUI for searching live logs
CONS OF SPLUNK
  • 1
    Splunk query language rich so lots to learn

related Splunk posts

Shared insights
on
Kibana
Splunk
Grafana

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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Snowflake logo

Snowflake

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The data warehouse built for the cloud
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PROS OF SNOWFLAKE
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    Good Performance
  • 1
    Public and Private Data Sharing
  • 1
    Multicloud
  • 1
    Great Documentation
  • 1
    Serverless
CONS OF SNOWFLAKE
    Be the first to leave a con

    related Snowflake posts

    Shared insights
    on
    Google BigQuery
    Snowflake

    I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

    What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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    Shared insights
    on
    Snowflake
    Hadoop
    MarkLogic

    For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

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    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
    • 48
      Fast and Flexible
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      One platform for every big data problem
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      Easy to install and to use
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      Great for distributed SQL like applications
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      Works well for most Datascience usecases
    • 2
      Machine learning libratimery, Streaming in real
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      In memory Computation
    • 0
      Interactive Query
    CONS OF APACHE SPARK
    • 3
      Speed

    related Apache Spark posts

    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.9M 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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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 941.1K 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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    Kubernetes logo

    Kubernetes

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    Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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    PROS OF KUBERNETES
    • 152
      Leading docker container management solution
    • 121
      Simple and powerful
    • 96
      Open source
    • 73
      Backed by google
    • 56
      The right abstractions
    • 24
      Scale services
    • 17
      Replication controller
    • 9
      Permission managment
    • 6
      Simple
    • 5
      Supports autoscaling
    • 5
      Cheap
    • 3
      Reliable
    • 3
      No cloud platform lock-in
    • 3
      Self-healing
    • 3
      Open, powerful, stable
    • 3
      Scalable
    • 3
      Promotes modern/good infrascture practice
    • 2
      Cloud Agnostic
    • 2
      Backed by Red Hat
    • 2
      Custom and extensibility
    • 2
      Quick cloud setup
    • 2
      Captain of Container Ship
    • 2
      A self healing environment with rich metadata
    • 1
      Everything of CaaS
    • 1
      Easy setup
    • 1
      Expandable
    • 1
      Runs on azure
    • 1
      Sfg
    • 1
      Golang
    • 1
      Gke
    CONS OF KUBERNETES
    • 13
      Poor workflow for development
    • 11
      Steep learning curve
    • 5
      Orchestrates only infrastructure
    • 2
      High resource requirements for on-prem clusters

    related Kubernetes posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 38 upvotes · 3.7M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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    Yshay Yaacobi

    Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

    Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

    After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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    HBase logo

    HBase

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    The Hadoop database, a distributed, scalable, big data store
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    PROS OF HBASE
    • 8
      Performance
    • 5
      OLTP
    • 1
      Fast Point Queries
    CONS OF HBASE
      Be the first to leave a con

      related HBase posts

      Hi, I'm building a machine learning pipelines to store image bytes and image vectors in the backend.

      So, when users query for the random access image data (key), we return the image bytes and perform machine learning model operations on it.

      I'm currently considering going with Amazon S3 (in the future, maybe add Redis caching layer) as the backend system to store the information (s3 buckets with sharded prefixes).

      As the latency of S3 is 100-200ms (get/put) and it has a high throughput of 3500 puts/sec and 5500 gets/sec for a given bucker/prefix. In the future I need to reduce the latency, I can add Redis cache.

      Also, s3 costs are way fewer than HBase (on Amazon EC2 instances with 3x replication factor)

      I have not personally used HBase before, so can someone help me if I'm making the right choice here? I'm not aware of Hbase latencies and I have learned that the MOB feature on Hbase has to be turned on if we have store image bytes on of the column families as the avg image bytes are 240Kb.

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