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Due to technological advancements and the ever-increasing demand for big data, the availability of cloud-based data analytics platforms is crucial for any organization. These platforms are highly configurable, elastic, and economically efficient for data storage, processing, and analysis. By the time we reach 2024, these platforms will continue improving features, tools, and technologies to support data decision-making processes. In this article, we have presented the ten cloud-based data analytics platforms in June 2024 and their main advantages.
Google Cloud BigQuery is still one of the top options for data analytics. It is a Cloud-Based Data Analytics Platform, a serverless data warehouse that allows users to run complex SQL queries across Google’s infrastructure. It is best at handling large datasets and offers a strong machine learning integration via BigQuery ML. It works with real-time analytics, making it ideal for businesses that require insights in real-time.
Key Features:
- Serverless architecture for business owners
- Real-time analytics capabilities
- Integration with Google Cloud AI and Machine Learning services
- Affordable pricing structure that makes users pay as they consume services
AWS Redshift is a petabyte-scale data warehousing solution that offers a range of data warehousing capabilities in the cloud. Due to Redshift’s scalability and integration with various data sources, enterprises use Redshift maximum. Redshift Spectrum allows users to run queries against exabytes of data in S3 without ever having to transfer the data to Redshift.
Key Features:
- High-performance data warehousing
- Compliance with the AWS environment
- Redshift Spectrum for querying S3 data
- Advanced security features
Azure Synapse Analytics is a full-stack analytics service where enterprises can perform big data processing and data warehousing. It offers unified data ingestion, data preparation, data management, and data serving capabilities for business intelligence analytics, and machine learning purposes. Azure Synapse is closely connected with other Azure services, and it is a reliable basis for data analytics.
Key Features:
- Integrated solutions for dashboards, reports, and operating alerts
- Learn about the integration between Azure Machine Learning and Power BI
- Another area where big data is exerting its influence is in the imperative to process data in real-time
- Scalable and secure
The system they deploy at Snowflake means that computations and storage are entirely distributed, making it possible to process data quickly and concurrently while still scaling without issues. The Snowflake platform is purpose-built to run in the cloud and provides users with the flexibility and extensibility features of the cloud alongside additional functionalities such as data sharing and collaboration. It provides the capability to support multi-cloud deployment, enhancing the likelihood of redundancy across the different cloud providers.
Key Features:
- One of the characteristics is the separation of the storage, and the compute for scale, assuming you’ll scale compute but keep storage relatively fixed or scale storage but keep compute fixed.
- Multi-cloud support
- The protection of information, as well as its availability and access for employees to work on a project together
- Advanced data governance features to make the controller's task easier so that it can focus more on value-adding activities.
IBM Cloud Pak for Data is an Enterprise suite for data and Artificial Intelligence aimed at changing how organizations capture, process, and analyze Data. It offers integrated governance and security on an end-to-end platform while covering data management from the first principle. IBM's cognitive computing feature is Watson, and it also has advanced analytics and artificial intelligence.
Key Features:
- Here, you will find a complete overview of the extensive data and AI platform
- Governing the integrated and secure environments
- Watson AI integration
- The continued support of hybrid and multi-cloud environments
Oracle Analytics Cloud is a comprehensive Data Analytics solution that incorporates AI and machine learning in conjunction with different types of analytics. It comes equipped with all the predefined data preparation, visualization, and reporting capabilities that users require to gain insights from the heaps of information available and serve business goals effectively. Oracle has been tightly designed and integrated with its cloud, characterized by best performance and scalability.
Key Features:
- Full-suite analytics tools
- Implementation of both Artificial Intelligence and Machine Learning
- This is due to the high performance and ability of the web app to scale up in case of a large volume of users
- Improved features of data representation
As a start-up company founded in 2013, Databricks provides users with a versatile big data analytics tool derived from Apache Spark. It offers shared notebooks, ACM for cluster management, and an augmented run-time for Apache Spark, which makes the process of data engineering and data analytics more efficient for big data projects. This feature aims to provide reliability and consistencies of the data for Databricks’ Delta Lake.
Key Features:
- There was a significant in choosing the best-unified data analytics platform
- Jupyter notebooks are ideal for teams who analyze data emanating from computer programs
- Apache Spark Runtime Optimization
- Delta Lake to make data continuously reliable
Tableau Online is a tool developed to be hosted in the cloud and can be used to prepare, share, and create interactive analytics. It integrates with multiple data sources and boasts excellent analytics features, though it is not very challenging to incorporate due to the need to install complex structures. Tableau as a tool is very popular and is easy to use due to the intuitive UI, and it offers several ways in which the data can be visualized.
Key Features:
- Tools and infrastructures for leveraging data to facilitate decision-making
- It is cloud-based, and therefore, the data is accessible anywhere and anytime by any authorized personnel
- The incorporation of many databases
- Real-time data connectivity
As an embedded team of Google Cloud, Looker is a modern data platform that helps in the smooth exploration, evaluation, and sharing of real-time business analytics. Looker has one inherently called LookML, which helps in developing models for large, complex models and allows the modeling of data components that can be reused. It improves when paired with Google Cloud, thus being a go-to tool for data analysis.
Key Features:
- This involves the analysis of data as it is collected and streamed to formulate real-time strategies
- LookML for dynamic data schema tuning
- It can also be integrated with Google Cloud Services if the user has some funds to invest in improving his or her site’s performance
- dynamic and easily distributable approaches
Qlik Sense is advanced data analysis software that employs an associative model to compute values that conventional relational queries and other this kind of BI tools do not discover. Self-service analytics services help users query the data and analyze it in an explorative manner to find new patterns. Hence, flexible and scalable, Qlik Sense is a multi-cloud visualization application platform that supports multiple types of data sources and deployment models.
Key Features:
- Exploratory data analysis on rosetta of data with an associative engine
- Self-service analytics capabilities
- Multi-cloud architecture
- Advanced tools for Data Visualization and Reporting
Key features to look for include scalability, ease of integration with other tools, real-time data processing, robust security measures, user-friendly interface, machine learning capabilities, cost-effectiveness, and strong customer support.
AWS Redshift is known for its deep integration with other AWS services and strong performance for complex queries, while Google BigQuery offers a serverless architecture with excellent scalability and integration with Google Cloud services. The choice depends on your specific use case and existing cloud infrastructure.
Yes, all top 10 platforms are designed to handle big data efficiently. They offer features like parallel processing, distributed storage, and advanced analytics capabilities to manage and analyze large datasets.
Most of these platforms offer free tiers or trial versions. For example, AWS Redshift and Google BigQuery provide limited free usage, while other platforms like Snowflake and Databricks offer free trials to test their services.
The top cloud-based data analytics platforms offer robust security measures, including data encryption (both at rest and in transit), identity and access management, compliance with industry standards (like GDPR and HIPAA), and regular security audits.
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