I have setup the lakebase which is in sync with lakehouse for enterpise application low latency access, we have enabled the below options

- Lakebase using Postgres 17,HTTPS Data API is helping to access database in easier way from Databricks Apps

- Integration sync between Lakehouse and Lakebase for API access

- It has branching option to maintain schema or feature changes

- Restore from previous point/time history - helps reducing recovery effort - Snapshots and backup support

- Autoscaling compute and suspend option

- Monitoring, logs and query metrics are giving visibility on active queries, performance and database health

- OAuth access and postgres role based connection is helping better security and controlled access

Lakebase pricing model with autoscaling and scale down option based on available compute pricing Review collected by and hosted on G2.com.

Lakebase all postgress features not available, so we can't directly migrate any existing postgres directly to lakebase

Scale down to zero not happens instantly faced some issues like disconnect from app for short time temporary pauses

Custom admin operations in database postgres are limited and not posisble Review collected by and hosted on G2.com.

I love using Databricks for bringing all our data together and making sure everything is more organized without isolated areas. The collaborative notebooks are fantastic because they allow me to work on code simultaneously with colleagues, which greatly speeds up the workflow. I also appreciate the Spark integration as it removes the hassle of manually configuring clusters. Databricks saves a lot of time due to the speed at which it processes data. The fact that it's cloud-based made the initial setup easy, which is another plus. Review collected by and hosted on G2.com.

Being a bit demanding, I would like them to improve the learning curve, because for someone just starting out it can be a little overwhelming due to the large number of configuration options, permissions, and governance settings. Review collected by and hosted on G2.com.

Delta Lake + Workflows + Unity Catalog in one platform eliminated the need for stitching together separate ingestion, transformation, and governance tools. As a data engineer, I spend more time building pipelines and less time managing infrastructure. The notebook experience and cluster auto-scaling make iterating on complex ETL fast and cost-efficient. Review collected by and hosted on G2.com.

Cluster spin-up times and cost predictability are still the biggest friction points for me. Cold starts can really slow down ad-hoc work, and DBU costs need close monitoring to avoid unpleasant surprises. The Workflows UI has improved a lot over time, but it still doesn’t feel as flexible as dedicated orchestrators when you’re dealing with more complex DAGs. Even so, I see these as mostly polish items—the platform’s core value easily outweighs them. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The ecosystem. What I like most about Databricks is how it removes a lot of the usual mess you run into with data work. Instead of juggling separate tools for engineering, analytics, and ML—and then spending extra time getting them to talk to each other—it brings everything into one place. That alone cuts down a lot of friction and saves time.

I also like the Lakehouse idea because it feels genuinely practical: you don’t have to choose between a data lake and a warehouse. You can work with one unified setup and still get performance when you need it.

On a day-to-day level, it’s also nice that different teams can collaborate in the same environment without constantly copying data around or rebuilding pipelines. Overall, it keeps things simpler and faster, especially when you’re iterating. Review collected by and hosted on G2.com.

What I don’t like about Databricks is that it can feel a bit heavy when you’re just trying to do something simple. There’s a lot going on under the hood, and while that’s great for scaling, it also comes with a learning curve. Things like clusters, configurations, and job setup take some time to get comfortable with.

Cost is another concern. Usage can creep up quickly if you’re not actively monitoring it, especially when teams can spin up compute freely. And at times, the overall experience feels a little fragmented across notebooks, jobs, and repos, rather than being one smooth, unified flow.

So, yes—it’s powerful, but it definitely takes discipline to keep things clean, efficient, and under control. Review collected by and hosted on G2.com.

I like how Databricks merges our warehousing and lakes into one architecture, eliminating the need to maintain separate storage and compute for operational reporting and predictive work on sensor data. This unified source of truth helps keep data duplication and sprawl in check while focusing on the operational picture. The Lakehouse concept of bringing warehousing and lake environments under one roof won me over. Unity Catalog simplifies governance by providing a clear view of data access, and Delta Live Tables efficiently manages our telemetry pipelines, reducing the effort needed for ensuring their reliability. I also find the initial setup quite easy with no bigger issues encountered. Review collected by and hosted on G2.com.

My one reservation is the cost side. The DBU model can climb quickly, and if cluster policies and auto-scaling limits go unattended, spend has a way of getting ahead of you. We saw this play out during an early stretch of heavy streaming, which pushed us to tighten our boundaries and keep a firm hand on utilization. Budget discipline rewards constant attention rather than a set-and-forget approach. I'd want clearer, real-time visibility into DBU spend at the cluster and workload level, with configurable alerts that flag when a job or fleet's consumption starts trending past its expected envelope before the bill lands, so budget governance becomes proactive rather than a monthly reconciliation exercise. Review collected by and hosted on G2.com.

I really appreciate that Databricks doesn't force a trade-off between control and convenience. My developers can access the cluster configuration for detailed tuning when necessary, but for routine tasks, the managed notebooks, job scheduling, and Unity Catalog take care of everything without needing a platform specialist. I like that Databricks borrows from software engineering practices instead of sticking to the classic analytics silo. The integration with version control, CI-friendly job definitions, and separated environments mean our data work follows the same review and release processes as the rest of our codebase. This consistency makes it easier to manage as the team grows. Review collected by and hosted on G2.com.

I find the on-ramp steeper than it should be for people who aren't already fluent in the ecosystem. When someone on my team picks it up for the first time, there's a long stretch where they're productive with the UI but not with what's happening underneath. Spark is the main pain point. When a job degrades or fails, the reasons are rarely obvious, and tracing a problem back to a skewed join or a bad shuffle usually eats far more of a senior developer's attention than it deserves. Richer query plan visualization, with plain-language explanations of why the optimizer made a given choice, would remove a lot of that guesswork and stop these investigations from landing on the same two or three people every time. Review collected by and hosted on G2.com.

What I like most about Databricks is how it centralizes data engineering, analytics, and AI in a single platform, which greatly facilitates the workflow on a daily basis. The integration between notebooks, pipelines, and distributed processing makes development faster and more organized, especially in projects with a large volume of data and automations.

Another point that I consider very strong is the experience with Apache Spark, integrated in a simplified way. Even in more complex scenarios, the performance is usually excellent, allowing large-scale data processing with good stability and scalability. This greatly helps in integrations, ETLs, and analyses that, in other solutions, would require much more effort. Review collected by and hosted on G2.com.

Although I quite like the platform, some aspects of Databricks can still be challenging. The main one is the cost, especially in environments with intensive processing or when clusters are not well optimized. Without more rigorous usage control, expenses can increase rapidly.

Another aspect is the learning curve, which can be steep for teams that are starting in the distributed data ecosystem. Concepts related to Spark, clusters, optimization, and resource management require time to adapt, especially for those coming from more traditional tools.

In UI/UX, although the interface is generally good, some administrative processes and more advanced configurations can seem confusing at first. In certain scenarios, identifying performance or permission issues may also require more technical knowledge. Review collected by and hosted on G2.com.

I really appreciate how Databricks earns its place in my work as the analytics tier for the reference architectures I propose to customers. It simplifies the process by allowing telemetry from distributed device fleets to land directly, enabling me to model aggregation, anomaly detection, and historical comparison without needing separate infrastructure. The platform's cost efficiency is a decisive factor for me, and it delivers more value than its price suggests. Retrieval speed is impressive, letting me query a warehouse live during design sessions without relying on others to run the request. The setup required far less scaffolding than anticipated, positively impacting how I prototype. Starting with Databricks didn't involve the tedious configuration I expected from a platform of its scale, so the environment was productive almost immediately. Also, the platform significantly shortens the path from an idea to a prototype that clients can see, thanks to its low cost, quick retrieval, and minimal setup requirements. Review collected by and hosted on G2.com.

Handling of unstructured content remains the weak point in my view, since building a usable search layer over message archives, PDF documents and finance spreadsheets proved awkward enough that I compromised on the design. Vector search performance disappoints as well: direct lookups return promptly, yet reasoning queries slow noticeably, which becomes apparent the moment a client is watching a demonstration. Review collected by and hosted on G2.com.

I really appreciate Databricks' ability to consolidate scattered infrastructure data like permitting records and contractor reporting into a single workspace, which makes spotting compliance gaps and making strategic decisions much easier. The broad data source connectivity is also a highlight for me because it bridges legacy systems with much less friction than I expected. Having Genie to pair with it has been a game changer, allowing me to build, test, and deploy data objects quickly, keeping my analysis moving without stalling. The initial setup was surprisingly simple, taking just a few hours with no major problems. These features make Databricks valuable for someone like me who needs reliable data more than deep engineering expertise. Review collected by and hosted on G2.com.

My one reservation concerns the Python editor. It serves its purpose, yet it could stand to be more robust. A richer editing experience would make the occasional bit of custom scripting feel less cumbersome when I am refining a data object. I'd like to see richer code intelligence in the Python editor, things like smarter autocomplete, inline error highlighting, and better debugging tools, so that when I write custom scripts to shape a data object, the process feels smoother and I catch mistakes before running the code rather than after. Review collected by and hosted on G2.com.