Amazon Redshift vs Qubole

Overview

Qubole

Stacks36

Followers104

Votes67

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

Amazon Redshift vs Qubole: What are the differences?

1. Performance and Scalability: Amazon Redshift is designed for high-performance analytical workloads and can handle large amounts of data efficiently. It uses columnar storage and parallel processing to provide fast query performance. On the other hand, Qubole is a cloud-native data platform that offers an elastic and scalable infrastructure to process big data workloads. It can dynamically scale resources based on the workload's requirements, allowing for efficient resource utilization.
2. Data Integration and Ecosystem: Amazon Redshift provides seamless integration with various data sources including Amazon S3, Amazon EMR, and other databases within the AWS ecosystem. It also supports a wide range of third-party tools and services. In contrast, Qubole offers integrations with various data sources and tools such as Hive, Presto, and Spark. It provides connectors for popular cloud storage systems like Amazon S3 and Azure Blob Storage.
3. Cost and Pricing Model: Amazon Redshift offers different pricing options, including on-demand pricing and reserved instance pricing, allowing users to choose the most suitable pricing model for their needs. Qubole, on the other hand, offers flexible pricing options based on the resources used and the data processed. Its pricing model includes factors such as the number of compute nodes, storage usage, and data transfer.
4. Data Security and Governance: Amazon Redshift provides robust data security features such as encryption at rest and in transit, IAM access control, and data masking. It also supports integration with AWS Identity and Access Management (IAM) for fine-grained access control. Qubole also offers enterprise-grade data security features, including encryption and access control. It provides integration with various authentication mechanisms like LDAP and Active Directory.
5. Data Warehousing Capabilities: Amazon Redshift provides advanced data warehousing capabilities, including support for complex queries, data compression techniques, and workload management features. It also offers features like distributed backups and automated cluster management. Qubole, on the other hand, provides a unified data platform with built-in data warehousing capabilities. It offers features like data discovery, data lineage, and data cataloging.
6. Managed Service: Amazon Redshift is a fully managed service provided by AWS, which means that it takes care of the infrastructure management, software updates, and backups. This allows users to focus on their data analysis tasks without worrying about the underlying infrastructure. Qubole also offers a fully managed platform, taking care of the infrastructure management and resource optimization, enabling users to focus on data processing and analysis.

In Summary, Amazon Redshift and Qubole differ in terms of their performance and scalability, data integration and ecosystem, cost and pricing model, data security and governance, data warehousing capabilities, and managed service offerings.

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Advice on Qubole, Amazon Redshift

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Comments

Detailed Comparison

Qubole	Amazon Redshift
Qubole is a cloud based service that makes big data easy for analysts and data engineers.	It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Intuitive GUI;Optimized Hive;Improved S3 Performance;Auto Scaling;Spot Instance Pricing;Managed Clusters;Cloud Integration;Cluster Lifecycle Management	Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>
Statistics
Stacks 36	Stacks 1.5K
Followers 104	Followers 1.4K
Votes 67	Votes 108
Pros & Cons
Pros 13 Simple UI and autoscaling clusters 10 Feature to use AWS Spot pricing 7 Real-time data insights through Spark Notebook 7 Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters 6 Hyper elastic and scalable	Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption
Integrations
Google Compute Engine Microsoft Azure	SQLite MySQL Oracle PL/SQL

What are some alternatives to Qubole, Amazon Redshift?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

Cloudera Enterprise

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

Airbyte

It is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses lakes & DBs.

Treasure Data

Treasure Data's Big Data as-a-Service cloud platform enables data-driven businesses to focus their precious development resources on their applications, not on mundane, time-consuming integration and operational tasks. The Treasure Data Cloud Data Warehouse service offers an affordable, quick-to-implement and easy-to-use big data option that does not require specialized IT resources, making big data analytics available to the mass market.

Related Comparisons

Amazon Redshift vs Qubole: What are the differences?

1. Performance and Scalability: Amazon Redshift is designed for high-performance analytical workloads and can handle large amounts of data efficiently. It uses columnar storage and parallel processing to provide fast query performance. On the other hand, Qubole is a cloud-native data platform that offers an elastic and scalable infrastructure to process big data workloads. It can dynamically scale resources based on the workload's requirements, allowing for efficient resource utilization.
2. Data Integration and Ecosystem: Amazon Redshift provides seamless integration with various data sources including Amazon S3, Amazon EMR, and other databases within the AWS ecosystem. It also supports a wide range of third-party tools and services. In contrast, Qubole offers integrations with various data sources and tools such as Hive, Presto, and Spark. It provides connectors for popular cloud storage systems like Amazon S3 and Azure Blob Storage.
3. Cost and Pricing Model: Amazon Redshift offers different pricing options, including on-demand pricing and reserved instance pricing, allowing users to choose the most suitable pricing model for their needs. Qubole, on the other hand, offers flexible pricing options based on the resources used and the data processed. Its pricing model includes factors such as the number of compute nodes, storage usage, and data transfer.
4. Data Security and Governance: Amazon Redshift provides robust data security features such as encryption at rest and in transit, IAM access control, and data masking. It also supports integration with AWS Identity and Access Management (IAM) for fine-grained access control. Qubole also offers enterprise-grade data security features, including encryption and access control. It provides integration with various authentication mechanisms like LDAP and Active Directory.
5. Data Warehousing Capabilities: Amazon Redshift provides advanced data warehousing capabilities, including support for complex queries, data compression techniques, and workload management features. It also offers features like distributed backups and automated cluster management. Qubole, on the other hand, provides a unified data platform with built-in data warehousing capabilities. It offers features like data discovery, data lineage, and data cataloging.
6. Managed Service: Amazon Redshift is a fully managed service provided by AWS, which means that it takes care of the infrastructure management, software updates, and backups. This allows users to focus on their data analysis tasks without worrying about the underlying infrastructure. Qubole also offers a fully managed platform, taking care of the infrastructure management and resource optimization, enabling users to focus on data processing and analysis.

Amazon Redshift vs Qubole

Overview