Alternatives to Vertica logo

Alternatives to Vertica

MonetDB, HBase, Presto, Hadoop, and Cassandra are the most popular alternatives and competitors to Vertica.
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What is Vertica and what are its top alternatives?

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.
Vertica is a tool in the Databases category of a tech stack.
Vertica is an open source tool with GitHub stars and GitHub forks. Here’s a link to Vertica's open source repository on GitHub

Top Alternatives to Vertica

  • MonetDB
    MonetDB

    MonetDB innovates at all layers of a DBMS, e.g. a storage model based on vertical fragmentation, a modern CPU-tuned query execution architecture, automatic and self-tuning indexes, run-time query optimization, and a modular software architecture. ...

  • 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. ...

  • Presto
    Presto

    Distributed SQL Query Engine for Big Data

  • 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. ...

  • 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. ...

  • 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. ...

  • Oracle
    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

  • Druid
    Druid

    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations. ...

Vertica alternatives & related posts

MonetDB logo

MonetDB

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Column-store database
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PROS OF MONETDB
  • 2
    High Performance
CONS OF MONETDB
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    related MonetDB posts

    HBase logo

    HBase

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    The Hadoop database, a distributed, scalable, big data store
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    PROS OF HBASE
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      Performance
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      OLTP
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      Fast Point Queries
    CONS OF HBASE
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      related HBase posts

      I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

      See more

      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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      Presto logo

      Presto

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      Distributed SQL Query Engine for Big Data
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      PROS OF PRESTO
      • 18
        Works directly on files in s3 (no ETL)
      • 13
        Open-source
      • 12
        Join multiple databases
      • 10
        Scalable
      • 7
        Gets ready in minutes
      • 6
        MPP
      CONS OF PRESTO
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        related Presto posts

        Ashish Singh
        Tech Lead, Big Data Platform at Pinterest · | 37 upvotes · 1.1M 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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        Eric Colson
        Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M 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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        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
        • 4
          Great load balancer
        • 1
          Amazon aws
        • 1
          Java syntax
        CONS OF HADOOP
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          related Hadoop posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.1M 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 )

          See more
          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.

          See more
          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
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            High performance
          • 80
            High availability
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            Easy scalability
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            Replication
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            Multi datacenter deployments
          • 26
            Reliable
          • 9
            OLTP
          • 7
            Open source
          • 7
            Schema optional
          • 2
            Workload separation (via MDC)
          • 1
            Fast
          CONS OF CASSANDRA
          • 3
            Reliability of replication
          • 1
            Updates

          related Cassandra posts

          Thierry Schellenbach
          Shared insights
          on
          RedisRedisCassandraCassandraRocksDBRocksDB
          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

          See more
          Umair Iftikhar
          Technical Architect at ERP Studio · | 3 upvotes · 228.1K 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.

          See more
          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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            Public and Private Data Sharing
          • 3
            Good Performance
          • 3
            Multicloud
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            Serverless
          • 2
            User Friendly
          • 2
            Great Documentation
          • 1
            Usage based billing
          • 1
            Innovative
          • 1
            Economical
          CONS OF SNOWFLAKE
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            Shared insights
            on
            Google BigQueryGoogle BigQuerySnowflakeSnowflake

            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
            SnowflakeSnowflakeHadoopHadoopMarkLogicMarkLogic

            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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            Oracle logo

            Oracle

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            An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
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            PROS OF ORACLE
            • 42
              Reliable
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              Enterprise
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              High Availability
            • 5
              Hard to maintain
            • 5
              Expensive
            • 4
              Maintainable
            • 3
              Hard to use
            • 3
              High complexity
            CONS OF ORACLE
            • 13
              Expensive

            related Oracle posts

            Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

            We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

            We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

            ASP.NET / Node.js / Laravel. ......?

            Please guide us

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            Druid logo

            Druid

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            Fast column-oriented distributed data store
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            PROS OF DRUID
            • 14
              Real Time Aggregations
            • 6
              Batch and Real-Time Ingestion
            • 4
              OLAP
            • 3
              OLAP + OLTP
            • 2
              Combining stream and historical analytics
            • 1
              OLTP
            CONS OF DRUID
            • 3
              Limited sql support
            • 2
              Joins are not supported well
            • 1
              Complexity

            related Druid posts

            Umair Iftikhar
            Technical Architect at ERP Studio · | 3 upvotes · 228.1K 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.

            See more