Need advice about which tool to choose?Ask the StackShare community!

Denodo

25
90
+ 1
0
Presto

367
932
+ 1
66
Add tool
Decisions about Denodo and Presto
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

See more
Karthik Raveendran
CPO at Attinad Software · | 3 upvotes · 154.4K views

The platform deals with time series data from sensors aggregated against things( event data that originates at periodic intervals). We use Cassandra as our distributed database to store time series data. Aggregated data insights from Cassandra is delivered as web API for consumption from other applications. Presto as a distributed sql querying engine, can provide a faster execution time provided the queries are tuned for proper distribution across the cluster. Another objective that we had was to combine Cassandra table data with other business data from RDBMS or other big data systems where presto through its connector architecture would have opened up a whole lot of options for us.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Denodo
Pros of Presto
    Be the first to leave a pro
    • 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

    Sign up to add or upvote prosMake informed product decisions

    What companies use Denodo?
    What companies use Presto?
      No companies found
      See which teams inside your own company are using Denodo or Presto.
      Sign up for StackShare EnterpriseLearn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with Denodo?
      What tools integrate with Presto?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      What are some alternatives to Denodo and Presto?
      AtScale
      Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
      Tableau
      Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
      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.
      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.
      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.
      See all alternatives