On August 6, 2020, Looker unveiled its new features for the first time since Google closed its $2.6 billion acquisition this year in February.
The new capabilities built on those in Looker 7 with tight Google Cloud Platform (GCP) integrations are the top-requested enhancements for system administrators, data analysts, and decision-makers, plus significant steps forward for application builders, marketers, and model developers. Cymetrix offers end to end Looker implementation services
With complete support for Google Marketing Analytics suite and an update to Looker Marketplace where customers can access and share newly built data experiences, Looker focuses on maximizing the impact of data analytics in marketing. It will help businesses to get data-rich business insights within a short time, increase user engagement, and create new revenue opportunities.
The new foundational enhancements and revamped Modern BI capabilities will help enterprises across the globe to create better and faster data solutions that transcend traditional BI, unlock new types of data experiences, accelerate data application development with new Data Products, manage enterprise-scale deployments, reduce development costs and maximize data infrastructure investment.
Here’s a crisp summary of Looker’s new top 10 features:
1. Looker Marketplace — Looker’s central location for finding, deploying, and managing Looker content including Looker Blocks, applications, visualizations, and plug-ins.
- Marketplace provides customers the ability to build, access, and share data experiences. Custom applications built by the developers/Looker community can be rapidly deployed within their own organization and made available to the entire Looker customer base via Marketplace – The home to all Looker Blocks including the recently launched COVID-19 Data Block.
2. New Looker Blocks — Pre-built pieces of code that can be leveraged for analytics of Google Marketing Platform (GMP) advertising and web data.
- These building blocks are fully-customizable, offer interactive data exploration, and new views of data based on light Machine Learning (ML) predictions. They are directly linked to GMP so that users can adjust campaigns and paths for pushing data insights back into GMP. For Example, A marketer just needs to pick what particular activity they‘re interested in analyzing — buying a product, signing up for a newsletter, requesting a demo — and Looker and BigQuery (Google Cloud’s serverless data warehouse) can predict what a visitor’s likelihood is of performing that action and then take it even further because Looker has built hooks back into the Google Marketing Platform to have these great insights.
- The new blocks for Google Analytics 360, Campaign Manager + Display & Video 360, Search Ads 360, and Google Ads are available on Looker Marketplace/Looker Blocks Directory. For example, For Google Analytics 360 block — Looker built in an advanced analytics model called “propensity to act.”
- The Blocks created with Google’s craftsmanship integrate most tightly with the Google platform, but non-Google data can be incorporated through application program interfaces as long as it’s a platform that can be queried with SQL. For example, customers can plug into sales automation platforms like Adobe Systems Inc.’s Marketo and Salesforce.com’s Salesforce for lead-to-opportunity-to-cash modeling and import the results into their enterprise resource planning system.
3. The Looker Data Dictionary is an extension — a web application built using Looker components which are developed using the Looker extension framework and deployed through the Looker Marketplace.
- It provides a dedicated user interface to help business users find the field and metrics they need for analysis. It also gives them the ability to quickly audit all LookML fields to help users understand what data sets are used most and which explores are duplicative. LookML is a language for describing dimensions, aggregates, calculations and data relationships in a SQL database.
- The Looker Data Dictionary extension provides a dedicated, centralized interface for searching through all the Looker fields and descriptions. It’s almost like a guide of the entire data model. The Data Dictionary is really useful for data geeks and enthusiasts because it helps them understand all the fields and the metrics that they need or that they’re using for their analysis (and) visualize the relationships between the data.
- The Data Analysts/Engineers can easily search the metadata with a Data Dictionary which helps the single-source-of-search for business users to search and trust all metric definitions. They can also Quickly audit and optimize all LookML fields for things like missing descriptions and repeat fields.
- The new framework for Looker 7 allows developers to customize data experiences within Looker and embed those experiences into existing enterprise applications and workflows.
- It is introduced for building widgets and applications and allows front-end developers to build and deploy applications within Looker — no DevOps or standalone servers are needed. It provides a dedicated and purpose-built UI to help business users find the field and metrics they need for analysis. It also gives them the ability to quickly audit all LookML fields for visualizing the relationship between datasets and explorers to understand what data sets are used the most and what explores are duplicative.
- Business users can take advantage of existing authentication and permissions while developers can focus on building great user experiences, and Looker will take care of the Hosting, DevOps, and Security.
5. Integrated Development Environment (IDE) — Looker’s newly redesigned IDE improves the overall experience for model developers. It makes model development, analysis, and workflows intuitive with more advanced controls.
- Looker has also added a new object browser that simplifies project navigation and efficiently tracks and reuses available objects. It also simplifies the ‘code-based’ modeling process and makes the workflow more intuitive with an increased focus on performance, usability, and approachability. A single repository has been added that’s shareable across instances to share model development, and folders to improve project organization and collaboration.
6. Cross-filtering — is another amazing BI feature for Looker that allows users to get more granular and meaningful data, quickly discover new customer insights and patterns by accessing underlying data in Looker dashboards.
- The users can slice and dice every field and with a single click, every level of detail and all the tiles are updated automatically as the users have access to all the levels of detail and don’t need to extract databases. This offers nearly endless opportunities for users to self-serve and surface new data points and insights.
7. Slack Integration — Enhanced Slack Integration provides users the ability to ask questions of data, and receive the answers back, right in the Slack chat environment with user-level authentication and granular permissions.
- With the updated Slack integration users can access their favorite Looker content, make data-driven conversations, and enjoy seamless sharing.
8. Aggregate awareness — Looker uses aggregate awareness logic to find the smallest, most efficient table available in the user’s database to run a query while still maintaining correctness.
- New updates help the users to create faster queries at a lower cost. For very large tables in the database, Looker developers can create smaller aggregate tables of data, grouped by various combinations of attributes. The aggregate tables act as roll-ups or summary tables that Looker can use for queries whenever possible, instead of the original large table.
- Basically, it tells Looker that there are aggregate tables within the database that have data at a higher level of detail. These are tables that can generate faster and more efficient queries because instead of analyzing billions of rows of very fine granular data, users can go up to aggregates of that data and automatically UNION larger data sets when necessary.
9. Persistent Derived Tables (PDT) — A Persistent Derived Table (or PDT for short) is a table that Looker can create and manage in a target database. The table is loaded with data from a SQL statement defined in a view file, and is refreshed on a regular basis. This is in contrast to Ephemeral Derived Tables, which are never written to the database, but are used in SQL queries like a database view.
- New Parallel PDT includes customization for customers to increase how many PDTs can be built at a time per connection, faster data downloads when streaming query results, and pooled database connections to reduce query latency. Developers can build aggregate tables right in their database via LookML and leverage them to query fewer rows, lower query costs, and provide results faster.
- Before, the user could only build PDTs one at a time, and now one can actually build them in parallel. It’s all for retaining the full power of modern data warehouses and optimizing the performance of Google BigQuery.
10. Looker System Administrators — Enhancements are also being released for System Admins which include an elite system activity manager that helps them understand everything that‘s going on with the Looker system, including the most popular queries or reports that are being run and the most active users.
- These features will help the System Admins to understand, analyze, and control system activity, manage users and groups across large enterprises, and to merge Looker-based permissions with those of corporate systems. It will give them the ability to manage their content, improve user adoption, and audit user data over time.
What’s Next For Looker?
In October, Looker will host its annual user conference, which it’s calling JOIN@Home this year, as a digital event.
It’s preparing another product announcement for the conference: the ability to use Looker on a mobile device.
Looker is more than a BI tool for people to consume data or analyze data on reports and dashboards. Looker really is a platform where people can build data applications, and they‘ve invested a lot in just making it easier, faster and to reduce the effort and complexity for people to build these data applications. Looker also will continue to leverage its opportunities with Google.
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Cymetrix offers end to end Looker implementation services that include assessment, business requirements gathering, implementation, data model design (LookML), ETL data integration, database migration, report prototype development, and interface design, embedded analytics with Looker APIs, and also offer detailed end-user training.
As a trusted Looker Consulting partner, Cymetrix’s experienced Looker consultants and developers implement robust Looker solutions and support the right platform features to empower every user in your organization to create, collaborate, and benefit from insights gained from Looker driven analytics to maximize return on your BI investments.