Looker Studio + Amazon Seller Central

How to use Looker Studio with code-free, ready-to-go data automation for Amazon Sellers

Google announced that it is rebranding its cloud BI product, Data Studio, into Looker Studio. This change will allow companies to quickly build custom dashboards and reports without having to learn a new tool. In addition, Google says that the look and feel of the dashboard will remain unchanged.

What is Looker Studio?

Looker Studio is a free web-based reporting tool that allows you to create data visualizations, dashboards, and reports. It’s easy to use and can be accessed from any device with an internet connection. You can insert charts, tables, maps, and more into your sales or marketing dashboard. Once you have created your report, you can share it with others or publish it on the web so that all of your team members can see the latest changes.

Looker Studio is the perfect option for people that want to graduate from using Excel or Google Sheets without the additional cost of other business intelligence tools.

Looker and Amazon, Better Together

We are excited to announce Looker Studio, like Google Data Studio, can leverage our Amazon Seller Central (SP-API) partner connectors. The SP-API, formerly known as Amazon MWS, delivers a new type of seller tool that gives business and marketing teams new analytics capabilities.

Looker Studio paired with Amazon Seller Central data is a powerful combination for sellers who want to create a simple dashboard or in-depth comprehensive reports and visualizations.

As a data source, Amazon Seller Central offers a default dashboard that allows you to manage your products, inventory, and orders. The Seller dashboard offers up key metrics on units ordered, total sales, and others.

What if you want to do a different type of analysis, report, or visualization? While the default Seller Central UI is sufficient, leveraging your own reporting tools, like Looker Studio, allows you to undertake an in-depth analysis of organic sales, wasteful ad spending, and search history.

You can connect to data directly from Amazon Seller Central via API and use this data directly within Looker Studio. With this connection, you can create more comprehensive reports for yourself quickly and easily.

What Is Possible With Looker Studio + Seller Central?

For data-driven Amazon merchants, the Amazon Selling Partner API (SP-API) connector will supply key business Insights so you can measure the impact of retail operations. The SP-API connector allows direct, code-free access to daily or real-time Seller Central data through Looker Studio.

Openbridge offers a type of connector for various Seller services;

• Amazon Inventory: Get FBA Inventory Reports data for the listing, condition, disposition, and quantity to help with day-to-day inventory.
• Amazon Fulfillment: Get comprehensive, product-level detail on inbound shipments, shipped FBA orders, quantity, tracking, and shipping with FBA Fulfillment Reports and Inbound Fulfillment API.
• Amazon Orders: Order and item information for both FBA and seller-fulfilled orders including order status, fulfillment and sales channel information, and item details with Order API, and FBA Orders Reports.
• Amazon Finance: Balances, payouts, estimated and actual selling, storage, and fulfillment fee data with FBA Settlement Reports, FBA Fees, and Finance API
• Detail Sales & Traffic: Business-level Sales and Traffic reports offer performance metrics for product sales, revenue, units ordered, and page traffic metrics such as page views and buy box.
• Vendor Central: Manage retail business operations with automated integration so vendors can improve and maintain performance at scale while growing business.
• Retail Analytics: Vendor Retail Analytics delivers ordered revenue, glance views, conversion, replenishable out-of-stock, lost buy box, returns, replacements, and many more.
• Brand Analytics: Brand Analytics offers sellers and vendors market basket analysis, search terms, repeat purchases, alternate purchases, item comparisons, and much more.

Amazon Advertising + Looker Studio

Looker Studio is a powerful tool for business users and marketers to fuse retail sales data with Amazon Advertising data from the Amazon Demand-side Platform (Amazon DSP) and Amazon Attribution services.

Connect Shopify to Looker Studio

Connecting Looker Studio and Shopify is available with pre-built, ready-to-go connectors. See the Shopify connector for more details.

Looker Studio + Google BigQuery

Looker Studio works best when paired with a native connector to Google BigQuery.

If you don’t already have a Google Cloud account for this tool (and if not, I highly recommend signing up), doing so only takes a couple of minutes. Plus, we can offer an additional $200 in free credits!

The Google Cloud Platform Free Trial + additional credits from our partners provide you with hundreds of dollars to try the Cloud Platform. Whether you are building software or automobiles, looking to create the next big thing, or wanting to put your company on the same infrastructure that powers Google, Google Cloud Platform has the technology and the partners to help grow your business on our Cloud.

The process of connecting Looker Studio to BigQuery is simple and ready-built by Google. Now, you can use BigQuery and Looker Studio to create your own dashboards for Amazon Seller Central. This is a great way to make a dashboard in Looker Studio that is more flexible and powerful than the reporting UI that Amazon provides.

Looker Studio pricing

Looker Studio is free, though there is a Looker Studio Pro version that Google charges for. For more information on pricing for the Pro version, Google requires you to talk to a Cloud sales specialist.

Looker Studio Alternatives

Not interested in Looker Studio? Explore, analyze, and visualize data to deliver faster innovation while avoiding vendor lock-in using tools like Tableau, Microsoft Power BI, Looker, Amazon QuickSight, SAP, Alteryx, dbt, Azure Data Factory, Qlik Sense, and many others.

Get Started Now

Openbridge has developed the automated API connector code, which means there is nothing for your to program. We are official connector providers for Amazon Selling Partner and Amazon Advertising API. Openbridge is an Amazon Advertising Partner tools provider and is an approved PII data supplier for Seller Central.

Fuel financial forecasting, marketing analysis, sales reporting, and marketing optimization. Create a new custom dashboard, enhance an existing dashboard, or perform a breakdown of sales with Looker Studio.

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Looker Studio + Amazon Seller Central was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Databricks Lakehouse: Why?

Databricks Lakehouse

When to use Databricks and what role should it play in a data lakehouse architecture

Not to oversimplify, but the “lakehouse” is marketing speak for the fusion of the data lake and cloud warehouse. The lakehouse concept is not new, though commercial labeling by companies like Databricks, Google, AWS, Microsoft, and others is.

In this post, we will cover the lakehouse model and how Databricks fits.

What is a lakehouse?

A data lakehouse should be defined as a modern data platform that draws on the best aspects of both a data lake and a data warehouse. The pairing of the storage of a data lake with the query services of a data warehouse is commonly referred to as a lakehouse.

A well-abstracted, standards-based data architecture, by design, depends on loosely coupled services at all levels of the modern data stack. For example, traditional data warehouses tightly couples compute (ie query processing) and storage ( database, tables, views…). However, the lakehouse separation of those compute and storage can deliver efficiency, scalability, and flexibility.

Ideally, one would find a common approach to the advocacy of a modern data strategy that embraces a vendor-agnostic, well-abstracted, standards-based data architecture. While it sounds simple enough, it is not uncommon to see the exact opposite; architectures that are vendor-driven, tool specific, and tightly coupled.

As such, it can be challenging to sift through all the noise. For example, in our post Sorry, Data Lakes Are Not “Legacy” an idea advocated by Fivetran was that you should pick Snowflake and declare victory.

Lauren Balik highlighted the phenomenon in How Fivetran + dbt actually fail. Lauren details how vendors advocate “modern data stacks” that play into a pricing/revenue model, not one based on sound architecture and open, well-abstracted, and open services.

What is lakehouse architecture?

It is important to define the core principles of architecture and technical values. While focused on Facebook Presto, the folks at Ahana discussed the topic of well-abstracted, open data stacks in their post The SQL Data Lakehouse. As discussed by the team Ahana Cloud, the arguments about a Databricks lakehouse vs Snowflake data mesh are unfortunately more marketing one-upmanship than one of the technical merits.

For example, Amazon Athena is a serverless query engine that has no local storage. Storage is the domain of the data lake, which Athena can query. This allows query and storage to scale horizontally.

While Athena, which is based on Facebook Presto, was designed to support this type of local de-coupling of processing and storage, it is by no means the only player. The pattern is quickly becoming a staple, including traditional data warehouse vendors attempting to extend their core offerings to support this model. Ahana, Databricks, Redshift Spectrum, Snowflake, BigQuery, and a host of others support some level of decoupled storage and compute.

The lakehouse takes the scalable, persistent object storage of the data lake with the SQL, management features, data cataloging, and tools from data warehouses. As a result, the benefits of open, flexible standards in the face of constant technological advances offer welcomed agility and flexibility.

In a paper called Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics, the use of architecture offers low-cost storage in an open format accessible by a variety of processing engines for compute services is the foundation of the data lakehouse;

We define a Lakehouse as a data management system based on low-cost and directly-accessible storage that also provides traditional analytical DBMS management and performance features such as ACID transactions, data versioning, auditing, indexing, caching, and query optimization. Lakehouses thus combine the key benefits of data lakes and data warehouses: low-cost storage in an open format accessible by a variety of systems from the former, and powerful management and optimization features from the latter.

The paper concludes by advocating a modern data architecture that we have promoted (before it was dubbed a lakehouse). The lakehouse architecture that offers SQL and other processing compute services over a well-designed data lake, using open, standard-base file formats, provides flexibility, scale, and competitive price-performance concerns for query latency.

Optimizations, such as localized cache stores in tools like Tableau, can often alleviate query latency performance concerns due to the underlying storage model. As a matter of fact, most modern advanced analytics tools like Power BI, Looker, Tableau, and others embrace a relatively standard approach to optimizing performance via caching.

As we stated earlier, any modern data architecture, by design, must depend on a loosely coupled separation of compute and storage to deliver an efficient, scalable, and flexible solution.

Pentaho co-founder and CTO James Dixon, who coined the term “data lake”, said;

This situation is similar to the way that old school business intelligence and analytic applications were built. End users listed out the questions they want to ask of the data, the attributes necessary to answer those questions were skimmed from the data stream, and bulk loaded into a data mart. This method works fine until you have a new question to ask. The approach solves this problem. You store all of the data in a lake, populate data marts and your data warehouse to satisfy traditional needs, and enable ad-hoc query and reporting on the raw data in the lake for new questions.

A well-abstracted data lake architecture is an excellent foundation for any lakehouse. The benefits of open, flexible standards in the face of constant technological advances provide resistance to your data or analytics process. This includes extending your cloud data warehouses to query the content of your data lake. For example, you may have a use case where your data lake is extended for use by Snowflake or Databricks, or if Snowflake became cost-prohibitive, leverage Ahana or Athena.

As a result, a lakehouse Databricks, Ahana, Snowflake….solution must offer a unified approach for a data platform that can efficiently support business analysts’ analytics capabilities including the use of AWS Analytics with QuickSight, use of Power BI, Tableau, or other tools. So use one business intelligence tool, or any of them, data consumption is well-abstracted so each tool can perform queries against the same lakehouse.

AWS, Google, or Azure Databricks Cloud Data Lakehouse

A lakehouse is not limited to a single platform. Most major cloud platforms package lakehouse solutions. AWS, Google, and Azure all offer a variation of a lakehouse architecture based on their specific product offerings.

For example, the Amazon Web Services Lake House relies on Amazon Athena, Amazon S3, Kinesis, QuickSight, and many other services;

Microsoft also offers a lakehouse vision leveraging Azure services;

Google Cloud puts BigQuery at the core of its offering;

Google highlights the separation of compute and storage as central to its lakehouse data platform;

In the Google example and earlier example from AWS, they illustrate how Amazon Athena can just as easily consume data from a lake as Redshift, Databricks, or Presto.

In all the examples above the architecture rely on a well-abstracted data lake. We have written at length about the benefits of a data lake model as foundational elements of your overall data architecture. A data lake does not intrinsically carry any more technical debt than a warehouse. A data lake, done well, can help reduce the risk of incurring technical debt by properly abstracting aspects of your data architecture with best-in-class solutions. For example, a data lake can offer velocity and flexibility in employing different compute services, minimizing risks of vendor lock-in, and mitigating switching costs.

What is the Databricks Lakehouse?

The Databricks Lakehouse, done properly, should continue a pattern of a well-abstracted, unified approach we advocate for as a characteristic of a modern data platform.

The Databricks LakeHouse is typically offered as a cloud service that provides a platform for ingesting and analyzing data from multiple sources. This includes data ingestion of data objects residing in external locations Hadoop Distributed File System (HDFS), Apache Kafka, Amazon S3, Azure Data Lake Storage Gen2, Azure Blob Storage, and many others.

What is Databricks Lakehouse architecture?

The architecture of a Databricks lakehouse is similar to best practices and reference architectures we have detailed for Azure, Google, and AWS. For example, the AWS data lake uses serverless SQL query services like Amazon Athena and Amazon Redshift (Redshift Spectrum) to query the contents of your data lake.

As a result, a Databricks lakehouse should follow best practices, including embracing compute and storage abstractions and leveraging modern query engine designs resident in many tools.

Databrick or no Databricks, the key is that the core of your lakehouse architecture is not tightly coupled to any specific vendor implementation (unless you make a strategic decision to do so).

Is Delta Lake a Lakehouse?

Databricks refers to a “Delta Lake” in the same context as its “lakehouse.” Databricks says the following;

“Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs. Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns.”

As such, Delta Lake is not a Lakehouse but may be part of one if you decide on the type of functionality needed for your use case.

The Databricks Unified Analytics Platform

As we have stated, an open, well-abstracted data platform means you can run queries from various tools like Domo, Tableau, Microsoft Power BI, Looker, Amazon Quicksight, and many others.

In addition to supporting a broad array of industry-leading analytic tools, it supports consumption by tools and software like Python, DBT, Tableau Prep, Azure Data Factory, AWS Glue, Azure Machine Learning, and hundreds of others.

So a “Databricks Unified Analytics Platform” should first and foremost, be a function of thoughtful and vendor-agnostic architecture.

Why?

We have touched on a number of points already, but another consideration to consider is that given the high rate of change in the analytics and data tools ecosystems, a data platform must avoid tight coupling with vendor solutions. Tight coupling may offer some benefits, but at the expense of increased technical debt, reduced flexibility, and reliance on niche technological know-how.

As such, data consumption tools should embrace this ethos of playing nicely in a well-abstracted data platform, given that the platform should always be agnostic to the tools that consume data from it. A properly designed Databricks lakehouse must meet that challenge.

Azure Databricks SQL Analytics

With SQL Analytics, you can connect to your data lake without having to install additional software. Many tools offer native connectivity to Databricks which ensures your broader data team can be productive and efficient.

For example, Tableau Databricks offers built-in connectors so a business user consuming a report or a data scientist performing a deep-dive analysis can easily connect to the enterprise Databricks data lakehouse and quickly be productive. Microsoft Power BI and Looker also support native connectivity to Databricks.

So Tableau, Power BI, Looker, DBT, and many others offer native support for Databricks which affords you choice in choosing the tools that offer your teams the greatest flexibility and productivity.

What is Databricks SQL?

Databricks SQL is a dedicated space within Databricks for SQL users. It provides a first-class environment for SQL users, allowing you to run SQL queries against large datasets without having to worry about managing infrastructure.

SQL Analytics workspace features a clean UI designed specifically for SQL users. This includes a powerful set of tools for exploring data, visualizing it, and analyzing it. In a pinch, you can even use SQL Analytics to build dashboards and reports. This might be useful for prototypes or data exploration in support of more formal work in Tableau or PowerBI.

Getting started with Databricks Lakehouse

Whether you are looking at a Lakehouse for Retail or a Lakehouse for Financial Services, Ecommerce, or something else entirely, the right architecture is critical.

Getting started can be intimidating. We always suggest that you start small and be agile in pilot projects. Even going through demos with sample datasets can be excellent next steps. Cloud providers will offer the resources needed to explore Databricks as a free trial. For example, AWS offers a Databricks 14-day free trial on AWS. There is free Databricks training as well.

Being a successful early adopter means taking a business value approach rather than a technology outcome.

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Databricks Lakehouse: Why? was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.

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