Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
SAP Cloud ERP
Score 8.6 out of 10
N/A
SAP Cloud ERP (formerly SAP S/4HANA Cloud) is a modular ERP that enables users to run mission-critical operations in real time from anywhere, introduce new business models in any industry, and expand globally. SAP Cloud ERP is a SaaS product and can also be deployed in a hybrid landscape for quicker time to value. SAP Cloud ERP is a foundational component of the
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
Does well Manufacturing companies - E2E integration. Product Costing integration with manufacturing. Order to Cash - E2E integration, Joule, and openness to integrating third-party products. Less appropriate AI capabilities - competition is moving faster. E2E professional services business as business is evolving faster, and new ideas are coming in at a fast pace.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
The software helps in forecasting and identifying potential challenges that would affect our organisation in the near future. This enables us to prepare in advance and also adopt practical and viable measures.
It enables faster and accurate data processing, which enhances decision-making.
It's easily customizable according to specific organizational needs. This promotes overall efficiency and productivity of the organization.
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
The cost of SAP as an ERP is quite high and the switching costs associated with ERP systems are even higher. That being said moving from one ERP to another only happens once in a great while for large organizations. Those switching costs include retraining, IT hardware requirements, outside consultants and more
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
Day to day data insight is more accurate for manufacturing industry to procure as per forecasted from supplier. Supply and fulfillment cycle becomes more easier. I would say more about performance as we are using this new server so we can see clear difference between SAP S/4HANA Cloud and ECC. Also it has customized business extensions for rapid development.
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
The support system I find great as whenever I run into problems they rectify them quickly as possible they never reacted late the support is just up to the mark for me. They provide many solutions to the problems I faced the [technical] team support is always amazing they [listen] to mean work accordingly.
SAP requires a lot of internal and external resources to complete its successful implementation. The cloud version requires a deeper understanding of the different capabilities of the local systems (hardware) and the connection towards your local IT team. We found several problems on our systems that we couldn't foresee before the implementation and roll out.
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
The platform utilizes advanced predictive analytics to anticipate operational bottlenecks and put them out of commission before the problems become larger. We can proactively develop effective strategies that help keep service quality in the face of unexpected changes in the market, or external disruptions, by continuously analyzing historical performance data as well as elements of the current market
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
