Amazon Redshift vs Microsoft Azure Synapse Analytics vs OpenText Analytics Database (Vertica) comparison

Amazon Redshift is a fully administered, petabyte-scale cloud-based data warehouse service. Users are able to begin with a minimal amount of gigabytes of data and can easily scale up to a petabyte or more as needed. This will enable them to utilize their own data to develop new intuitions on how to improve business processes and client relations.

Initially, users start to develop a data warehouse by initiating what is called an Amazon Redshift cluster or a set of nodes. Once the cluster has been provisioned, users can seamlessly upload data sets, and then begin to perform data analysis queries. Amazon Redshift delivers super-fast query performance, regardless of size, utilizing the exact SQL-based tools and BI applications that most users are already working with today.

The Amazon Redshift service performs all of the work of setting up, operating, and scaling a data warehouse. These tasks include provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine.

Amazon Redshift Functionalities

Amazon Redshift has many valuable key functionalities. Some of its most useful functionalities include:

• Cluster administration: The Amazon Redshift cluster is a group of nodes that contains a leader node and one (or more) compute node(s). The compute nodes needed are dependent on the data size, amount of queries needed, and the query execution functionality desired.
  
• Cluster snapshots: Snapshots are backups of a cluster from an exact point in time. Amazon Redshift offers two types of snapshots: manual and automated. Amazon will store these snapshots internally in the Amazon Simple Storage Service (Amazon S3) utilizing an SSL connection. Whenever a Snapshot restore is needed, Amazon Redshift will create a new cluster and will import data from the snapshot as directed. 
  
• Cluster access: Amazon Redshift provides several intuitive features to help define connectivity rules, encrypt data and connections, and control the overall access of your cluster.
  
• IAM credentials and AWS accounts: The Amazon Redshift cluster is only accessible by the AWS account that created the cluster. This automatically secures the cluster and keeps it safe. Inside the AWS account, users access the AWS Identity and IAM protocol to create additional user accounts and manage permissions, granting specified users the desired access needed to control cluster performance.
  
• Encryption: Users have the option to choose to encrypt the clusters for additional added security once the cluster is provisioned. When encryption is enabled, Amazon Redshift will store all the data in user-created tables in a secure encrypted format. To manage Amazon Redshift encryption keys, users will access AWS Key Management Service (AWS KMS).

Reviews from Real Users

“Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there.” - Kundan A., Senior Consultant at Dynamic Elements AS

“With the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features. They have enriched and elaborate documentation that is helpful.”- Aishwarya K., Solution Architect at Capgemini

Microsoft Azure Synapse Analytics is an end-to-end analytics solution that successfully combines analytical services to merge big data analytics and enterprise data warehouses into a single unified platform. The solution can run intelligent distributed queries among nodes, and provides the ability to query both relational and non-relational data.

Microsoft Azure Synapse Analytics is built with these 4 components:

1. Synapse SQL
2. Spark
3. Synapse Pipeline
4. Studio

Microsoft Azure Synapse Analytics Features

Microsoft Azure Synapse Analytics has many valuable key features, including:

• Cloud Data Service: WIth Microsoft Azure Synapse Analytics you can operate services (data analytics, machine learning, data warehousing, dashboarding, etc.) in a single workspace via the cloud.
  
  
• Structured and unstructured data: Microsoft Azure Synapse Analytics supports both structured and unstructured data and allows you to manage relational and non-relational data - unlike data warehouses and lakes that tend to store them respectively.
  
  
• Effective data storage: Microsoft Azure Synapse Analytics offers next-level data storage with high availability and different tiers.
  
  
• Responsive data engine: Microsoft Azure Synapse Analytics uses Massive Parallel Processing (MPP) and is designed to handle large volumes of data and analytical workloads efficiently without any problems.
  
  
• Several scripting languages: The solution provides language compatibility and supports different programming languages, such as Python, Java, Spark SQL, and Scala.
  
  
• Query optimization: Microsoft Azure Synapse Analytics works well to facilitate limitless concurrency and performance optimization. It also simplifies workload management.

Microsoft Azure Synapse Analytics Benefits

Some of the benefits of using Microsoft Azure Synapse Analytics include:

• Database templates: Microsoft Azure Synapse Analytics offers industry-specific database templates that make it easy to combine and shape data.
  
  
• Better business insights: With Microsoft Azure Synapse Analytics you can expand discovery of insights from all your data and apply machine learning models to all your intelligent apps.
  
  
• Reduce project development time: Microsoft Azure Synapse Analytics makes it possible to have a unified experience for developing end-to-end analytics, which reduces project development time significantly.
  
  
• Eliminate data barriers: By using Microsoft Azure Synapse Analytics, you can perform analytics on operational and business apps data without data movement.
  
  
• Advanced security: Microsoft Azure Synapse Analytics provides both advanced security and privacy features to ensure data protection.
  
  
• Machine Learning: Microsoft Azure Synapse Analytics integrates Azure Machine Learning, Azure Cognitive Services, and Power BI.

Reviews from Real Users

Below are some reviews and helpful feedback written by Microsoft Azure Synapse Analytics users who are currently using the solution.

PeerSpot user Jael S., who is an Information Architect at Systems Analysis & Design Engineering, comments on her experience using the product, saying that it is “Scalable, intuitive, facilitates compliance and keeps your data secure”. She also says "We also like governance. It looks at what the requirements are for the company to identify the best way to ensure compliance is met when you move to the cloud."

Michel T., CHTO at Timp-iT, mentions that "the features most valuable are the simplicity, how easy it is to create a dashboard from different information systems."

A Senior Teradata Consultant at a tech services company says, "Microsoft provides both the platform and the data center, so you don't have to look for a cloud vendor. It saves you from having to deal with two vendors for the same task."

OpenText Analytics Database Vertica is known for its fast data loading and efficient query processing, providing scalability and user-friendliness with a low cost per TB. It supports large data volumes with OLAP, clustering, and parallel ingestion capabilities.

OpenText Analytics Database Vertica is designed to handle substantial data volumes with a focus on speed and efficient storage through its columnar architecture. It offers advanced performance features like workload isolation and compression, ensuring flexibility and high availability. The database is optimized for scalable data management, supporting data scientists and analysts with real-time reporting and analytics. Its architecture is built to facilitate hybrid deployments on-premises or within cloud environments, integrating seamlessly with business intelligence tools like Tableau. However, challenges such as improved transactional capabilities, optimized delete processes, and better real-time loading need addressing.

• Fast Data Loading: Quickly ingests large data volumes.
• Columnar Storage: Optimizes storage efficiency and speed.
• OLAP Support: Provides robust analytics capabilities.
• Advanced Compression: Enhances storage efficiency and performance.
• Parallel Data Ingestion: Enables faster data processing.
• Fault Tolerance: Ensures high availability and reliability.

• Cost Efficiency: Low cost per TB is advantageous.
• Scalability: Accommodates growing data needs seamlessly.
• Integration Flexibility: Works well with BI tools like Tableau.
• Real-Time Reporting: Delivers immediate insights for decision-making.

OpenText Analytics Database Vertica's implementation spans industries such as finance, healthcare, and telecommunications. It serves as a central data warehouse offering scalable management, high-speed processing, and geospatial functions. Companies benefit from its capacity to integrate machine learning and operational reporting, enhancing analytical capabilities.