Databricks Data Intelligence Platform
Databricks, Inc.External reviews
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Databricks Unifies Teams with Strong IaC, Streaming, and Git Integration…!!!
What do you like best about the product?
I like Databricks since it has improved collaboration between our data science and data engineering teams by bringing their workflows onto one platform.Its also the best since it offers us with a complete Terraform provider for managing infrastructure as code makes streaming data processing straightforward and integrates with multiple Git providers with a built-in merge assistant to simplify version control.
What do you dislike about the product?
I have no complain regarding Databricks.
What problems is the product solving and how is that benefiting you?
Databricks streamlines data processing and analytics by unifying them on a single platform.
Powerful Low-Latency Telemetry Pipelines with Streaming Tables & Materialized Views
What do you like best about the product?
In a telco environment handling massive data volumes from fixed and mobile networks (GPON, 4g/5g Core, and RAN) ingesting unstructured or semi-structured frequency telemetry incrementally from our virtualized functions like vEPC, vCPE or VHGW) with minimal setup.
My team works closely with virtualized network functions and Multi-access Edge Computing. Features like Streaming Tables and Materialized Views help us to build low-latency pipelines that process network performance metrics near real-time, helping us monitor network KPIs and QoS efficiency.
Because my team's core experties lies in network deisgn and system virtualization rather than database administration, Predictive Opimization and Liquid Clustering are highly beneficial. Tehy autonomously handle table maintenance, file compaction, and data layout optimization freeing up our resources to focus on network architecture.
My team works closely with virtualized network functions and Multi-access Edge Computing. Features like Streaming Tables and Materialized Views help us to build low-latency pipelines that process network performance metrics near real-time, helping us monitor network KPIs and QoS efficiency.
Because my team's core experties lies in network deisgn and system virtualization rather than database administration, Predictive Opimization and Liquid Clustering are highly beneficial. Tehy autonomously handle table maintenance, file compaction, and data layout optimization freeing up our resources to focus on network architecture.
What do you dislike about the product?
Virtualized network functions, routers, and disaggregated hardware frequently undergo software updagrades, which often introduce sublte changes in telemetry output schemas. When using structured streaming or auto loader these schema drifts cause our streaming queries to fail, requiring a manual restart of the stream to re-plan the schema.
When we need to update the logic of a complex network KPI defined within a materialized view, any change to the query triggers a full recomputation of the view. Given the massive scale of telecom transaction datasets, this can result in noticeable compute costs.
We rely on a variety of data tools within our ICT ecosystem, not all solutions featured in Partner Connect natively support Unity Catalog. This can crete integration and governance hurdles when we try to connect certain third-party analytics and data preperation tools to our secured data lake.
When we need to update the logic of a complex network KPI defined within a materialized view, any change to the query triggers a full recomputation of the view. Given the massive scale of telecom transaction datasets, this can result in noticeable compute costs.
We rely on a variety of data tools within our ICT ecosystem, not all solutions featured in Partner Connect natively support Unity Catalog. This can crete integration and governance hurdles when we try to connect certain third-party analytics and data preperation tools to our secured data lake.
What problems is the product solving and how is that benefiting you?
We ingest continous streas of performance data from virualized network functions and traditional transport layers. By building streaming pipelines, we can monitor virtualized cpres and routers to identify anomalies or degredations in network traffic.
Aligning with my interest in Network AI and Machine learning, our data scientists use the patform to develop predictive models. We train models on historical GPON/DSL line failures, mobile cell tower loads, and customer usage patterns to predict network congestion, schedule proactive maintenance and mitigate customer chirn across customer segments.
As an evangelist for tech evolution, I use the platform to bridge the gap between our core network engineering teams and business units. By connecting business semantics and establisihng secure Delta Sharing protocols, we provide business analysts and decision makers with giverned, self service access to network insights without risking security compliance.
Aligning with my interest in Network AI and Machine learning, our data scientists use the patform to develop predictive models. We train models on historical GPON/DSL line failures, mobile cell tower loads, and customer usage patterns to predict network congestion, schedule proactive maintenance and mitigate customer chirn across customer segments.
As an evangelist for tech evolution, I use the platform to bridge the gap between our core network engineering teams and business units. By connecting business semantics and establisihng secure Delta Sharing protocols, we provide business analysts and decision makers with giverned, self service access to network insights without risking security compliance.
Love the Databricks and its Features and Unity Catalog for Streamlined Governance
What do you like best about the product?
In Databricks, I really like the newer features such as Gennie, the Databricks Assistant, agents, and the event-trigger mechanism.
Also, the Unity Catalog feature is amazing. Having one place for all sources makes things much easier, and UC helps with governing tables in a more organized way.
Also, the Unity Catalog feature is amazing. Having one place for all sources makes things much easier, and UC helps with governing tables in a more organized way.
What do you dislike about the product?
Nothing special to dislike, but there’s a feature to jump to a particular command. The feature itself is fine, but it’s placed right next to the notebook, which makes it easy to click accidentally, and that breaks my workflow.
What problems is the product solving and how is that benefiting you?
I am using it in my project for data processing and data quality analysis. With Databricks and its functionality, I am building agents in Genie space. Using UC, I am managing all managed and external tables in one place.
All-in-One Platform for Data Engineering, ML, and GenAI
What do you like best about the product?
On one platform, we’re getting everything we need, including data engineering, machine learning, and GenAI.
What do you dislike about the product?
As of now, I don’t see any dislikes that impact my work.
What problems is the product solving and how is that benefiting you?
An end-to-end platform for deploying ML projects in one place.
As for us: From ML Proof of Concept to Secure Production Apps—Fast, Serverless, and Well-Governed
What do you like best about the product?
I personally like using this app for the ability to quickly transition a machine laerning or retrievel-augmented generation proof of concept into a secure, production-ready appliatinn using familiar python frameworks like streamlit gradio or dash. Running on serlerless compute means, that our team can demonstrate value to clients quickly without waiting for seperate instructure provisining.
We like also like unity catalog, that provides unified governance across structured and unstructured data, ml models and bussiness metrics. Features like Automated Lineage simplify client compliance audits.
We like also like unity catalog, that provides unified governance across structured and unstructured data, ml models and bussiness metrics. Features like Automated Lineage simplify client compliance audits.
What do you dislike about the product?
Yes, there is a thing to be improved. In strucutred streaing and auto loader, a schema change mid-execution causes the query to fail and requres a manual restart. For client production environments with strict SLAs, these interruptions can increase maintenance overhead.
What problems is the product solving and how is that benefiting you?
For clients seeking immediate analytics without a costly multi-mounth migration, we deploy Lakehouse federation. This feature help our team to query and govern data in place across disparate systems directly from the Databricks interface, accelerating the delivery of strategic insights.
We are also leveraging built-in tools like Mosaic AI vector search to build RAG applications, and utilizing Databricks aps to deliver interacive predictive analytics front-ends to clients. Overall, this is how we utilize this software.
We are also leveraging built-in tools like Mosaic AI vector search to build RAG applications, and utilizing Databricks aps to deliver interacive predictive analytics front-ends to clients. Overall, this is how we utilize this software.
Straightforward Collaboration in Shared Notebooks and Workflows
What do you like best about the product?
Collaboration is straightforward: data engineers, analysts, and data scientists can work together in shared notebooks and workflows.
What do you dislike about the product?
Debugging can be challenging—troubleshooting distributed jobs is often more complex than working with traditional SQL systems.
What problems is the product solving and how is that benefiting you?
Integration with cloud services like S3, Kafka, Glue, and Redshift is easier and more straightforward.
Databricks in my case: Multiple Integrations, Intuitive UI, and Reliable Performance
What do you like best about the product?
What I like most about Databricks is its Integrations part. In workplace, we integrate Database within multiple data soucres. Also, I can't complete my review without mentioning UX and UI design, which makes the overall workflow feel intuitive and genuinely user-friendly. When it comes to speed of the processes, it never offended us. It works as expected. Comparitevly from the market pricing, the price of the service is quite reliable for us. There is a help center of the Databricks, if you can't find any answers to your questions, there are specialists that may assist you with your inqurires. As an instance, I can remember the case where we had an issue within exam process, they helped us to solve this problem.
What do you dislike about the product?
From dislikes the ai quality of Genie. Guys it could be improved, especially the reasoning part. Also, I can say the case when we had an issue with exam process. Specialists helped us, but it took us little discomforties. Well,
What problems is the product solving and how is that benefiting you?
In aviation, we utilize this software for data analysis. We automized a lot of processes, which simple workplace tools can not handle. We also, integrate with multiple tools (names which I can not mention for securirty purposes) Particularly, it helps us to analyze passenger demand by route and season. We combine and analyze big datasets using this software. Overall, a good tool. Out team is satisfied.
Streamlined Data Management and Transformation
What do you like best about the product?
I use Databricks for storing and consuming data. I really like the unified catalog feature, as it helps me manage permissions and access to metadata easily. The ability to publish datamart data to Thoughtspot is beneficial, and I find data transformation using Databricks notebooks particularly helpful. The ease of initial setup with Databricks was great and our team of over 1000 people transitioned smoothly from Hadoop.
What do you dislike about the product?
Table level access. Provision to restrict access at table level is required.
What problems is the product solving and how is that benefiting you?
I use Databricks for storing and consuming data, with a unified catalog for easy access to metadata. The ability to transform and publish datamart data to Thoughtspot is valuable, though I'd like improved table-level access restrictions.
Databricks Simplifies Big Data Processing and Team Collaboration
What do you like best about the product?
What I like best about Databricks is how it simplifies large-scale data processing and collaboration in one platform. The integration with Spark and cloud service makes handling big data much more efficient. I also like the notebook environment, which makes it easier for teams for works together on analytics and machine learning tasks.
What do you dislike about the product?
One thing I dislike about Databricks is the platform can feel complex for new users, especially when managing clusters and configurations. Pricing can also become expensive with larger workloads if resources are not optimized carefully. While integrations and AI features are powerful, the onboarding process and support documentation could be more beginner-friendly.
What problems is the product solving and how is that benefiting you?
Databricks helps solve the challenge of processing and analyzing large amounts of data efficiently in one platform. It combines data engineering, analytics and AI workflows, which reduce the need for the multiple separate tools. This improves collaboration, speeds up data processing, and helps generate insights much faster.
Straightforward SQL, Smooth Workflow Scheduling, and a Handy Notebook Feature
What do you like best about the product?
It’s straightforward to write and run SQL, schedule workflows, and I especially like the notebook feature. Genie AI is helpful for diagnosing bugs, and it can also answer ad hoc questions whenever I need it.
What do you dislike about the product?
Genie’s AI feature could still use some improvement. It sometimes takes a long time to respond, and with more complex problems it doesn’t always handle them well.
What problems is the product solving and how is that benefiting you?
The workflow is very easy to schedule. It’s also simple to set up alerts, and the visualization makes it easy for me to modify and debug.
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