Apache Hadoop and Microsoft Azure Synapse Analytics are major players in the data management and analytics domain. User feedback suggests that Microsoft Azure Synapse Analytics holds an edge in feature richness, while Apache Hadoop is preferred for its open-source affordability.
Features: Apache Hadoop provides a distributed file system with high throughput and scalability, ideal for managing diverse data types including video and machine-generated data. Its HDFS is highly valued for cost-effective large data set storage. In contrast, Microsoft Azure Synapse Analytics excels in integration with Power BI, robust analytics, and data warehousing capabilities, offering scalability and user-friendly analytics functions.
Room for Improvement: Apache Hadoop needs advancements in memory handling and more intuitive visualization tools. The platform's open-source nature can complicate support and integration. Microsoft Azure Synapse Analytics users have expressed concerns about pricing models, initial setup challenges, and a need for improved machine learning functionalities and support documentation.
Ease of Deployment and Customer Service: Apache Hadoop, often an on-premises solution, can be difficult to deploy and relies on community-driven support, requiring substantial expertise. Meanwhile, Microsoft Azure Synapse Analytics is primarily cloud-based, offering smooth Microsoft tool integration. However, non-technical users may find deployment challenging, and there's a call for enhanced customer service.
Pricing and ROI: Apache Hadoop's cost-effectiveness is appealing, with no licensing fees due to its open-source nature, making it especially attractive for handling large data volumes. Microsoft Azure Synapse Analytics, with its pay-as-you-go model, provides scalability but involves potentially high costs, which may deter smaller businesses despite its optimized resource usage.
Some of my customers have indeed seen a return on investment with Microsoft Azure Synapse Analytics as they used it for analytics to drive decision-making, improving their processes or increasing revenue.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
They are slow to respond and not very knowledgeable.
This is an underestimation of the real impact because we use big data also to monitor the network and the customer.
I would rate the support for Microsoft Azure Synapse Analytics as an eight out of ten.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Microsoft Azure Synapse Analytics is scalable, offering numerous opportunities for scalability.
For the scalability of Microsoft Azure Synapse Analytics, I would rate it a 10 until you remain in the Azure Cloud scalability framework.
Recovering from such scenarios becomes a bit problematic or time-consuming.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
Performance and stability are absolutely fine because Microsoft Azure Synapse Analytics is a PaaS service.
I find the service stable as I have not encountered many issues.
We have never integrated Microsoft Azure Synapse Analytics with Databricks, but we have mostly pulled data from on-premises systems into Azure Databricks.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
Microsoft Azure Synapse Analytics is an excellent product because it includes both SIEM and orchestration capabilities with playbooks.
There is a need for better documentation, particularly for customized tasks with Microsoft Azure Synapse Analytics.
Databricks is a very rich solution, with numerous open sources and capabilities in terms of extract, transform, load, database query, and so forth.
The cheapest tier costs about $4,000 to $4,700 a year, while the most expensive tier can reach up to $300,000 a year.
I think the price of Microsoft Azure Synapse Analytics is very expensive, but that's not only for Microsoft Azure Synapse Analytics—it's for the cloud in general.
I find the pricing of Microsoft Azure Synapse Analytics reasonable.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
One of the most valuable features in Microsoft Azure Synapse Analytics is the ability to write your own ETL code using Azure Data Factory, which is a component within Synapse.
Microsoft Azure Synapse Analytics offers significant visibility, which helps us understand our usage more clearly.
For Microsoft Azure Synapse Analytics, the integration is the most valuable feature, meaning that whatever you need is fast and easy to use.
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 18 |
| Large Enterprise | 56 |
Apache Hadoop provides a scalable, cost-effective open-source platform capable of handling vast data volumes with features like HDFS, distributed processing, and high integration capabilities.
Apache Hadoop is known for its distributed file system HDFS, which supports large data volumes efficiently. Its open-source nature allows cost-effective scalability and compatibility with tools like Spark for enhanced analytics. While it offers significant processing power, areas for improvement include user-friendliness, interface design, security measures, and real-time data handling. Users benefit from data storage for structured and unstructured data, facilitated by its distributed processing architecture. Data replication ensures fault tolerance, while its capability to integrate with tools like Apache Atlas and Talend highlights its versatility.
What are the key features of Apache Hadoop?Industries leverage Apache Hadoop for Big Data analytics, data lakes, ETL tasks, and enterprise data hubs, handling unstructured and structured data from IoT, RDBMS, and real-time streams. Its applications extend to data warehousing, AI/ML projects, and data migration, employing tools like Apache Ranger, Hive, and Talend for effective data management and analysis.
Microsoft Azure Synapse Analytics integrates data warehousing and big data analytics seamlessly. It provides scalability and user-friendly features for efficient, real-time reporting and data management.
Azure Synapse Analytics is designed for seamless data integration, allowing users to scale their operations effectively while providing extensive analytics capabilities. It supports both traditional data warehousing and big data solutions with real-time reporting through an interactive interface that integrates well with Power BI. The platform's serverless flexibility optimizes cost while ensuring robust security, leveraging users' familiarity with SQL technologies. Scalability allows processing of large datasets efficiently, empowering companies to connect disparate data sources and support industry-specific needs. Despite its strengths, Synapse users often seek improved governance, schema management, and technical support. Enhanced integration with Microsoft and third-party tools, along with better data loading capabilities, are also desired.
What are the key features of Microsoft Azure Synapse Analytics?Azure Synapse Analytics is extensively implemented across sectors like healthcare, finance, marketing, and government. Organizations use it to build data pipelines, perform analytics modeling, and facilitate reporting. It supports data transformation, migration, and orchestration, enhancing business intelligence and decision-making capabilities by efficiently handling big data and connecting disparate data sources.
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