Looker is a business intelligence platform with an analytics-oriented application server that sits on top of relational data stores. The Looker platform includes an end-user interface for exploring data, a reusable development paradigm for creating data discovery experiences, and an extensible API set so the data can exist in other systems. Looker enables anyone to search and explore data, build dashboards and reports, and share everything easily and quickly.
Based on our record, Looker should be more popular than Amazon Kinesis Firehose. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
First, you may not know the Kinesis Data Firehose service. Here's the AWS definition: Amazon Kinesis Data Firehose is an Extract, Transform, and Load (ETL) service that captures, transforms, and reliably delivers streaming data to data lakes, data stores, and analytics services. (https://aws.amazon.com/kinesis/data-firehose/). - Source: dev.to / about 1 year ago
As you can see in the diagram, we are feeding all events from Event Bus via a catch-all rule into Kinesis Data Firehose. Firehose is a fully managed service that streams into specific destinations like Data Warehouses or Data Lakes. Unlike it's bigger brother of using Kinesis Data Streams directly, there are no setting up of shards and it's mostly configuration free. We are only defining a buffer interval which is... - Source: dev.to / over 1 year ago
When using EventBridge I always log all events to an S3 bucket for auditing, analytics and debugging purposes. A super easy method to do this is to create a Kinesis Data Firehose stream and create a rule that captures all events that points to the Firehose stream. The Firehose stream can then flush the events on S3 in an interval/size of choice based on configuration. - Source: dev.to / over 1 year ago
Have you looked at Kinesis Firehose? It was pretty much build for this use case although you will still need to see if you can define a partitioning scheme probably in combination with an S3 Select query to meet your query requirements. https://aws.amazon.com/kinesis/data-firehose/?nc=sn&loc=0. - Source: Hacker News / almost 2 years ago
Is continuous backup important ? e.g. If the stuff fails for one day and you lose that day's upload is that ok? Do you want it to push updates more frequently than once a day? If you want to continuously push updates then Kinesis Firehose might be worth looking into. Source: over 2 years ago
Then in the "foldername" you can have 5 folders, each one for each of the groups. This means that when group1 enters looker.com, his default page will be the "foldername", which contains group1folder (he cannot see the rest of the folders if you have set the permissions correctly for each folder). Source: about 1 year ago
Even if you want to make Wide Tables, combining fact and dimensions is often the easiest way to create them, so why not make them available? Looker, for example, is well suited to dimensional models because it takes care of the joins that can make Kimball warehouses hard to navigate for business users. - Source: dev.to / over 1 year ago
We take daily snapshots of test results, aggregate them, and send Looker dashboards to the appropriate teams. - Source: dev.to / about 2 years ago
Dashboard: I like to use Datastudio because it's easy (just like using google sheets), but you can also try out Looker. Source: over 2 years ago
For Growth and larger, I would recommend Looker. The only reason I wouldn't recommend it for the smaller company stages is that the cost is much higher than alternatives such as Metabase. With Looker, you define your data model in LookML, which Looker then uses to provide a drag-and-drop interface for end-users that enables them to build their own visualizations without needing to write SQL. This lets your... - Source: dev.to / over 2 years ago
Analytics Canvas - Analytics Canvas is a data management platform with a specific focus on Google data tools, enabling self-serve data preparation and automation for those working with Analytics, Ads, Search Console, Sheets, BigQuery, Data Studio and more.
Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Alteryx - Alteryx provides an indispensable and easy-to-use analytics platform for enterprise companies making critical decisions that drive their business strategy and growth.
Sisense - The BI & Dashboard Software to handle multiple, large data sets.
Talend Data Preparation - Talend Data Preparation combines intuitive self-service data preparation and data curation tools with data integration to accelerate data usage across the organization.
Microsoft Power BI - BI visualization and reporting for desktop, web or mobile