Looker is one of several leading business intelligence (BI tools) that can help your organization harness the power of your data and glean impactful insights that allow you to make the best decisions for your business.
Keep reading for a high-level overview of Looker’s key features, pros and cons of Looker versus competitors, and a list of tools and technologies that easily integrate with Looker to augment your reporting.
Overview of Looker
Looker is a powerful BI tool that can help a business develop insightful visualizations. Among other benefits, users can create interactive and dynamic dashboards, schedule and automate the distribution of reports, set custom parameters to receive alerts, and utilize embedded analytics.
Why Use Looker
If you’re looking for a single source of truth, customized visuals, collaborative dashboards, and top-of-the-line customer support, Looker might be the best BI platform for you. Being fully browser-based cuts down on confusion as your team gets going, and customized pricing means you get exactly what you need.
Pros of Looker
Looker offers performant and scalable analytics on a near-real-time basis.
Because you need to define logic before creating visuals, it enforces a single-source-of-truth semantic layer.
Looker is completely browser-based, eliminating the need for desktop software.
It facilitates dashboard collaboration, allowing parallel development and publishing with out-of-the-box git integration.
Cons of Looker
Looker can be more expensive than competitors like Microsoft Power BI; so while adding Looker to an existing BI ecosystem can be beneficial, you will need to take costs into consideration.
Compared to Tableau, visuals aren’t as elegant and the platform isn’t as intuitive.
Coding in LookML is unavoidable, which may present a roadblock for report developers who have minimal experience with SQL.
Select Complementary Tools and Technologies for Looker
Any SQL database
Amazon Redshift
AWS
Azure
Fivetran
Google Cloud
Snowflake
Was this high-level overview of Looker helpful? If you’d like to learn more about Looker reporting or discuss how other leading BI tools, like Tableau and Power BI, may best fit your organization, contact us to learn more.
The content of this blog is an excerpt of our Business Intelligence Tool Comparison Guide. Click here to download a copy of the guide.
When using a modern data warehouse, like Google BigQuery, your organization will likely see improved access to your data and dramatically improved analytics. If you pair a modern data warehouse with a BI tool, like Power BI, or a data science platform, like Dataiku, your organization can more quickly gain access to impactful insights that help you fuel innovation and drive business decisions.
In this post, we’ll provide a high-level overview of Google BigQuery, including a description of the tool, why you should use it, pros and cons, and complementary tools and technologies.
Overview of Google BigQuery
Google’s homegrown columnar database service, BigQuery was built for querying massive data sets. It is deeply embedded within the GCP ecosystem with pricing charged by the amount of data queried.
Value Prop:
Ability to train and use machine learning models right in the database
More control in how data is partitioned
Scalability:
All resource provisioning done by Google behind the scenes, so the infrastructure is managed for the organization
Performance:
BigQuery transparently and automatically provides highly durable, replicated storage in multiple locations and high availability with no extra charge
Materialized views allow for accelerated query performance and reduced costs
Features:
BigQuery BI Engine: an in-memory analysis service built into BigQuery that enables users to analyze large and complex datasets interactively with sub-second query response time and high concurrency
A high-speed streaming insertion API provides a powerful foundation for real-time analytics
Security:
Data encrypted at rest and in transit by default
Ability to restrict data access for users at the column level
Why Use Google BigQuery
With very little to manage, BigQuery excels at analyzing extremely large data sets. Existing GCP customers can leverage the tight integration with other GCP services.
Pros of Google BigQuery
Fully managed platform that does not require downtime for updates and automatically ensures high availability and geo-redundancy (physical separation of data centers that span multiple geographic locations to increase resiliency)
Low cost of storage with speed among the best in the industry for very large data sets
BigQuery Omni allows for querying data across cloud platforms, including Azure, AWS, and GCP
Excels at analyzing extremely large data sets and automatically uses artificial intelligence to optimize storage
Cons of Google BigQuery
Queries that have not been performance-tuned and queries returning a lot of redundant data can become costly very quickly
Works best with flat tables, which can make managing an enterprise data model difficult
Tooling support outside of the GCP ecosystem is often lacking compared to other platforms
Select Complementary Tools and Technologies for Google BigQuery
Google AI Hub
Google Cloud Dataflow
Google Data Studio
Dataiku
Looker
Tableau
We hope you found this high-level overview of Google BigQuery helpful. If you’re interested in learning more about Google BigQuery or other modern data warehouse tools like Amazon Redshift, Azure Synapse, and Snowflake, contact us to learn more.
The content of this blog is an excerpt of our Modern Data Warehouse Comparison Guide. Click here to download a copy of that guide.
In 2020, a year where enterprises had to rethink their business models to stay alive, Google Cloud was able to grow 47% and capture market share. If you are not already looking at Google Cloud as part of your cloud strategy, you probably should.
Google has made conscious choices about not locking in customers with proprietary technology. Open-source technology has, for many years, been a core focus for Google, and many of Google Cloud’s solutions can integrate easily with other cloud providers.
Kubernetes (GKE), Knative (Cloud Functions), TensorFlow (Machine Learning), and Apache Beam (Data Pipelines) are some examples of cloud-agnostic tools that Google has open-sourced and which can be deployed to other clouds as well as on-premises, if you ever have a reason to do so.
Specifically, some of Google Cloud’s services and its go-to-market strategy set Google Cloud apart. Modern and scalable solutions like BigQuery, Looker, and Anthos fall into this category. They are best of class tools for each of their use cases, and if you are serious about your digital transformation efforts, you should evaluate their capabilities and understand what they can do for your business.
Three critical challenges we see from our enterprise clients here at 2nd Watch repeatedly include:
How to get started with public cloud
How to better leverage their data
How to take advantage of multiple clouds
Let’s dive into each of these.
Foundation
Ask any architect if they would build a house without a foundation, and they would undisputedly tell you “No.” Unfortunately, many companies new to the cloud do precisely that. The most crucial step in preparing an enterprise to adopt a new cloud platform is to set up the foundation.
Future standards are dictated in the foundation, so building it incorrectly will cause unnecessary pain and suffering to your valuable engineering resources. The proper foundation, that includes your project structure aligned with your project lifecycle and environments, and a CI/CD pipeline to push infrastructure changes through code will enable your teams to become more agile while managing infrastructure in a modern way.
A foundation’s essential blocks include project structure, network segmentation, security, IAM, and logging. Google has a multi-cloud tool called Cloud Operations for logs management, reporting, and alerting, or you can ingest logs into existing tools or set up the brand of firewalls you’re most familiar and comfortable with from the Google Cloud Marketplace. Depending on your existing tools and industry regulations, compliance best practices might vary slightly, guiding you in one direction or another.
DataOps
Google has, since its inception, been an analytics powerhouse. The amount of data moving through Google’s global fiber network at any given time is incredible. Why does this matter to you? Google has now made some of its internal tools that manage large amounts of data available to you, enabling you to better leverage your data. BigQuery is one of these tools.
Being serverless, you can get started with BigQuery on a budget, and it can scale to petabytes of data without breaking a sweat. If you have managed data warehouses, you know that scaling them and keeping them performant is a task that is not easy. With BigQuery, it is.
Another valuable tool, Looker, makes visualizing your data easy. It enables departments to share a single source of truth, which breaks down data silos and enables collaboration between departments with dashboards and views for data science and business analysis.
Hybrid Cloud Solutions
Google Cloud offers several services for multi-cloud capabilities, but let’s focus on Anthos here. Anthos provides a way to run Kubernetes clusters on Google Cloud, AWS, Azure, on-premises, or even on the edge while maintaining a single pane of glass for deploying and managing your containerized applications.
With Anthos, you can deploy applications virtually anywhere and serve your users from the cloud datacenter nearest them, across all providers, or run apps at the edge – like at local franchise restaurants or oil drilling rigs – all with the familiar interfaces and APIs your development and operations teams know and love from Kubernetes.
Currently in preview, soon Google Cloud will release BigQuery Omni to the public. BigQuery Omni lets you extend the capabilities of BigQuery to the other major cloud providers. Behind the scenes, BigQuery Omni runs on top of Anthos and Google takes care of scaling and running the clusters, so you only have to worry about writing queries and analyzing data, regardless of where your data lives. For some enterprises that have already adopted BigQuery, this can mean a ton of cost savings in data transfer charges between clouds as your queries run where your data lives.
Google Cloud offers some unmatched open-source technology and solutions for enterprises you can leverage to gain competitive advantages. 2nd Watch has helped organizations overcome business challenges and meet objectives with similar technology, implementations, and strategies on all major cloud providers, and we would be happy to assist you in getting to the next level on Google Cloud.
Often times, organizations want to modernize their applications to increase agility and efficiency, jumpstart growth and accelerate time to market. They are looking to build applications, which adopt new application architectures and cloud-native services without disrupting the business. In many cases, some modernization may already be occurring in pockets throughout the organization, but the complexity of proprietary IT stack, dependencies on legacy applications, and slow speed of migration has inhibited organizations from gaining the desired outcomes.
To achieve your modernization goals while maintaining flexibility to choose where applications reside, Google Cloud created Anthos. Anthos is a 100% software solution, designed to work where you want – on-premise or in the cloud. It brings the same Google Kubernetes Engine you would find in Google Cloud to your data center, providing the maximum hybrid flexibility for application placement. By abstracting the infrastructure from the application, Anthos allows your development teams to focus on building applications, not managing infrastructure.
For organizations embarking on their modernization journey or for where adopting Kubernetes seems intimidating, Google has taken this into consideration with Migrate for Anthos. Migrate for Anthos performs the heavy lifting on your existing applications and containerizes applications that benefit from containerization. It automates the extraction of existing applications from servers and VMs into containers, without having to rewrite or re-architect applications, eliminating much of the complexity that has inhibited your modernization efforts.
Once you embrace and adopting Kubernetes, hybrid application placement becomes easier with Anthos. Google Anthos provides a unified management experience across deployments, making everything from your binaries and your application configuration to your security policies and rollback processes, portable between on-premises and other public clouds. As a result, instead of training all your teams on several platforms, you train them once, preserving your existing investments utilizing a common management layer to help your teams deliver quality services with low overhead.
To help you through your modernization journey, 2nd Watch has created Hybrid Cloud Solutions with Google Anthos. Designed to accelerate your modernization effort by operationalizing your organization on Anthos and implementing Kubernetes quickly, you can progress on your digital transformational journey at the pace that works best for your development teams and accomplishes your organizational goals.
With more than 10-years assisting clients in transitioning from legacy compute to highly agile cloud native teams, our proven methodology is designed to enable you to become a multi-cloud organization with a consolidated view, at a speed that works best for your business.
2nd Watch’s Hybrid Cloud Solutions with Google Anthos includes:
Anthos workshop
Set-up and configuration of Anthos
Migrate for Anthos
Istio and configuration management
Optimize security, observability, and resiliency
Creation of post migration image update process
It’s never been easier for your organization to adopt a multi-cloud application architecture. Whether you are just starting your transformation or already well into the process of modernizing your applications, Google Anthos allows you to consolidate all your operations across on-premises, Google Cloud, and other clouds, while giving you the flexibility to run and move applications where you need them without added complexity. Download our datasheet for more details.
-Dusty Simoni, Sr Product Manager, Hybrid Cloud
Assumes <100 workloads. Pricing based on actual time and materials. Google Anthos, infrastructure, and networking are sold separately. Kubernetes training, application modernization or re-architecture, high-availability, and multi-data center implementations are additional. Assumes Migrate for Anthos is utilized and only supported Linux VMs.
Moving to Google Cloud from on premise or cloud virtual machines is the focus of many enterprise companies, while also optimizing your workloads for performance, scale, and security. Workloads and applications migrating to the cloud require careful consideration to determine when and how their architecture must be modernized to best leverage the value of cloud technologies.
We see cloud migration from 3 different lenses:
Migrate > Modernize
Modernize > Migrate
Build Cloud-Native
Migrate > Modernize is the typical ‘lift-and-shift’ model of migration helping companies get quickly to the cloud. This approach is most helpful for time-sensitive projects like datacenter exits or quick scale for a new product launch. The Google Cloud Platform supports a quick and easy virtual machine migration to cloud while also automatically providing you right sizing and sustained use discounts to save right away.
Modernize > Migrate is a newer model that companies are taking on, especially when they are focused on upskilling staff and allowing experimentation while dealing with an existing infrastructure lock-in. In this approach, many departments upgrade their technology applications while on premise and then move those workloads to the cloud. Google Cloud offers a great way to move to containers and Kubernetes through their Anthos platform. We’ll have more insight on this in a future blog.
Building Cloud-Native is an approach that works for smaller, greenfield projects. Application development teams that have budget allocated and want to innovate fast can use this model to build their application in the cloud directly. Google Cloud was built with the developer in mind, making it the chosen platform for those looking to build their own cloud-native applications. App Engine is a great choice for those looking for a platform-as-a-service (PaaS) to abstract away the complexity of infrastructure and focus on your application development.
2nd Watch applies our proven cloud migration methodology to each of these models ensuring we reduce any downtime in your move to Google Cloud, with a predictable schedule and reliable OpEx forecast. All of our cloud migrations include a thorough Cloud Modernization Readiness Assessment, design and build of the foundational cloud architecture, and the planning, migration, and testing of workloads in the cloud.
We are excited to officially share that we have joined the Google Cloud Partner Advantage Program, giving Google Cloud customers access to our 2nd Watch services and cloud experts!
We are building our Google Cloud practice in response to growing demand for the platform in the marketplace. Our team already has multiple Google Cloud certifications, including for hybrid cloud infrastructure with Anthos and hybrid cloud service mesh with Anthos, and we are actively hiring to support a strong Google Cloud sales pipeline. As part of the Google Cloud Partner Advantage Program, we will guide clients’ Google Cloud migration efforts as well as their optimization, DevOps, analytics, and innovative architecture projects.
“By joining the Google Cloud Partner Advantage Program, we enable our clients’ multi-cloud strategies, offering cloud infrastructure for specific workloads and providing the exemplary experience our clients have come to expect,” said Rich Lyons, Director of Field Alliances at 2nd Watch. “We’re seeing increased demand for Google Cloud in the marketplace, particularly among food services, financial services and manufacturing companies. We look forward to working closely with Google Cloud to serve the needs of our customers.”
By participating in Partner Advantage, companies can harness the power of the Google brand, the innovation of Google products, and the energy of the Google Cloud ecosystem to capitalize on the growing cloud computing opportunity across all segments: Small and Medium Business (SMB), Corporate, or Enterprise. With access to information, training and tools, the program will enable 2nd Watch to meet the unique needs of its customers.
