Data Analytics

is the process of collecting, cleaning, analyzing, and interpreting data to extract meaningful insights. Data analytics is a powerful tool that has become a core competency for most businesses, as it can help them to improve their operations, make better decisions, and gain a competitive advantage. Data anaylitics is widely used in a veriety of sectors, such as manufacturing, retail, finance, healthcare, maintenance. These are just a few examples of how data analytics is being used in businesses today. As the amount of data continues to grow, the potential benefits of it will only increase.

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William Bach Collection

Webb Hall

Dataflow is a managed service that allows you to run Apache Beam pipelines on Google Cloud Platform.

Apache Beam is a unified model for batch and streaming data processing.

Dataflow can be used for a variety of data analytics tasks, including:

Data ingestion: bring data from a variety of sources, including cloud storage, databases, and streaming data sources.
Data processing: process data using a variety of algorithms, including batch processing, streaming processing, and machine learning.
Data analysis: analyze data using a variety of tools, including BigQuery, Dataproc, and Data Fusion.
Data visualization: visualize data using a variety of tools, including Google Charts and Google Data Studio.
Data transformation: powerful tool for data cleaning and transformation. In combination with DLP can perform data encryption.

Dataflow can be a valuable tool for data analytics. It can help organizations improving data ingestion, processing, cleaning and transformation, analysis, visualization and encryption. This can lead to better decision-making, increased productivity, and reduced risk.

Here are some of the benefits of using Dataflow:

Ease of use: a managed service, so you don't need to worry about managing the underlying infrastructure.
Scalability: can scale to meet the needs of even the largest data processing workloads.
Cost-effectiveness: a cost-effective solution for data processing. You only pay for the resources you use.

If you are looking for a scalable, cost-effective, and easy-to-use data analytics solution, then Dataflow is a good option for you.

Here are some of the limitations of Dataflow:

*Dataflow is a managed service, so you don't have as much control over the underlying infrastructure as you would if you were using a self-managed solution.
*Dataflow can be more expensive than self-managed solutions if you only need to process small amounts of data.
*Dataflow can be more complex to use than self-managed solutions if you need to customize your data processing pipelines.

Overall, Dataflow is a powerful and versatile tool for data analytics. It can be used to solve a wide variety of data processing problems. However, it is important to weigh the benefits and limitations of Dataflow before deciding if it is the right solution for your needs.

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BigQuery is a fully managed, relational, columnary, petabyte-scale analytics data warehouse that enables businesses to analyze all their data very quickly. It can help organizations to improve their data analytics in a number of ways, including:

Data storage: store all of your data in a single place, regardless of its format. Consolidate your data and make it easier to manage.
Data processing: process your data more promptly, even if it is large or complex. Get insights from your data in reduced time.
Data analysis: provides a variety of tools and features for analyzing your data. This can help identifying trends, patterns, and anomalies in your data.
Data visualization: visualize your data in a variety of ways. This can help you communicating the insights you have gained from your data to others.
Data governance: provides a number of features for data governance to ensure your data is secure, accurate, and compliant with regulations.

Overall, BigQuery can be a valuable tool for data analytics. It can help organizations improving data storage, processing, analysis, visualization, and governance, leading to better decision-making, increased productivity, and reduced risk.

Here are some of the benefits of using BigQuery:

Fast data processing: BigQuery can process data very fast, even if it is large or complex. This can help you to get insights from your data more quickly.
Scalability: BigQuery is designed to scale to meet the needs of even the largest organizations. This can help you to grow your business without having to worry about your data analytics needs.
Cost-effectiveness: BigQuery is a cost-effective solution for data analytics. You only pay for the data you store and the queries you run.

If you are looking for a scalable, cost-effective, and fast data analytics solution, then BigQuery is a good option for you.

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Dataproc is a fully managed Hadoop and Spark service that allows you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. It helps you creating clusters quickly, managing them easily, and saving money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.

Dataproc can be used for a variety of data analytics tasks, including:

Data ingestion: bring data from a variety of sources, including cloud storage, databases, and streaming data sources.
Data processing: process data using a variety of algorithms, including batch processing, streaming processing, and machine learning.
Data analysis: analyze data using a variety of tools, including BigQuery, Dataproc, and Data Fusion.
Data visualization: visualize data using a variety of tools, including Google Charts and Google Data Studio.

Dataproc can be a valuable tool for data analytics. It can help organizations to improve their data ingestion, processing, analysis, and visualization. This can lead to improved decision-making, increased productivity, and reduced risk.

Here are some of the benefits of using Dataproc:

Ease of use: a managed service, so you don't need to worry about managing the underlying infrastructure.
Scalability: can scale to meet the needs of even the largest data processing workloads.
Cost-effectiveness: a cost-effective solution for data processing. You only pay for the resources you use.

If you are looking for a scalable, cost-effective, and easy-to-use data analytics solution with a capability to process a combination of Python code with SQL queries, then Dataproc is a good option for you.

Here are some of the limitations of Dataproc:

*Dataproc is a managed service, so you don't have as much control over the underlying infrastructure as you would if you were using a self-managed solution.
*Dataproc can be more expensive than self-managed solutions if you only need to process small amounts of data.
*Dataproc can be more complex to use than self-managed solutions if you need to customize your data processing pipelines.

Overall, Dataproc is a powerful and versatile tool for data analytics. It can be used to solve a wide variety of data processing problems. However, it is important to weigh the benefits and limitations of Dataproc before deciding if it is the right solution for your needs.

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Cloud Data Fusion is a fully managed, cloud-native, enterprise data integration service that helps you to quickly and easily build and manage data pipelines. It can be used to integrate data from a variety of sources, including cloud storage, databases, and SaaS applications.

Cloud Data Fusion can be used for a variety of data analytics tasks, including:

Data ingestion: ingest data from a variety of sources, including cloud storage, databases, and streaming data sources.
Data transformation: variety of modern and legacy systems, code-free transformations, conditionals and pre/post processing, alerting and notifications, and error processing provide a comprehensive data integration experience..
Data loading: Cloud Data Fusion can be used to load data into a variety of destinations, including cloud storage, databases, and machine learning models. Run and operate high-volumes of batch data pipelines. Integration with Datastream allows you to deliver change streams into BigQuery for continuous analytics. Use feasibility assessment for faster development iterations and performance/health monitoring for observability.
Data governance: Cloud Data Fusion can be used to govern data using a variety of features, including data lineage, data quality, and data access control. Search integrated datasets by technical and business metadata. Track lineage for all integrated datasets at the dataset and field level.

Cloud Data Fusion can be a valuable tool for data analytics. It can help organizations to improve their data ingestion, transformation, loading, and governance. This can lead to improved decision-making, increased productivity, and reduced risk.

Here are some of the benefits of using Cloud Data Fusion:

Code-free self-service: remove bottlenecks by enabling nontechnical users through a code-free graphical interface that delivers point-and-click data integration
Ease of use: a managed service, so you don't need to worry about managing the underlying infrastructure.
Scalability: scale to meet the needs of even the largest data integration workloads.
Cost-effectiveness: Cloud Data Fusion is a cost-effective solution for data integration. You only pay for the resources you use.

If you are looking for a scalable, cost-effective, and easy-to-use, code-free data analytics solution, then Cloud Data Fusion is a good option for you.

Here are some of the limitations of Cloud Data Fusion:

*Cloud Data Fusion is a managed service, so you don't have as much control over the underlying infrastructure as you would if you were using a self-managed solution.
*Cloud Data Fusion can be more expensive than self-managed solutions if you only need to integrate small amounts of data.
*Cloud Data Fusion can be more complex to use than self-managed solutions if you need to customize your data integration pipelines.

Overall, Cloud Data Fusion is a powerful and versatile tool for data analytics. It can be used to solve a wide variety of data integration problems. However, it is important to weigh the benefits and limitations of Cloud Data Fusion before deciding if it is the right solution for your needs.

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Looker is an enterprise platform for business intelligence, data applications, and embedded analytics. Looker helps you explore, share, and visualize your company's data so that you can make better business decisions.

Looker is built on top of Google Cloud Platform, which provides a scalable, reliable, and secure foundation for your data analytics needs. It also integrates with a wide range of other Google Cloud Platform services, such as BigQuery, Cloud Dataproc, and Cloud Dataflow.

Here are some of the benefits of using Looker:

Ease of use: Looker is very easy to use, even if you don't have any experience with data analysis.
Flexibility: can be used to analyze data from a variety of sources.
Power: offers a wide range of features for exploring, sharing, and visualizing data.
Scalability: can be scaled to meet the needs of even the largest organizations.
Security: built on top of Google Cloud Platform, which provides a scalable, reliable, and secure foundation for your data analytics needs.

If you are looking for an enterprise-grade business intelligence platform, then Looker is a good option to consider.

Here are some of the most popular use cases for Looker:

Exploring data: Looker can be used to explore data from a variety of sources. This can be used to identify trends, patterns, and outliers.
Sharing data: Looker can be used to share data with others. This can be used to collaborate on data analysis and decision-making.
Visualizing data: Looker can be used to visualize data in a variety of ways. This can be used to make data more understandable and accessible.
Embedded analytics: Looker can be used to embed analytics in websites and applications. This can be used to provide users with real-time insights into their data.

Looker is a powerful tool that can be used to improve decision-making, operations, and gain a competitive advantage. If you are looking for an enterprise-grade business intelligence platform, then Looker is a good option to consider.

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Looker is an enterprise platform for business intelligence, data applications, and embedded analytics. It helps you explore, share, and visualize your company's data so that you can make better business decisions.

Here are some of the benefits of using Looker:

Ease of use: very easy to use, even if you don't have any experience with data analysis.
Flexibility: can be used to analyze data from a variety of sources.
Power: offers a wide range of features for exploring, sharing, and visualizing data.
Scalability: can be scaled to meet the needs of even the largest organizations.
Security: built on top of Google Cloud Platform, which provides a scalable, reliable, and secure foundation for your data analytics needs.

If you are looking for an enterprise-grade business intelligence platform, then Looker is a good option to consider.

Here are some of the most popular use cases for Looker:

Exploring data: explore data from a variety of sources. This can be used to identify trends, patterns, and outliers.
Sharing data: share data with others. This can be used to collaborate on data analysis and decision-making.
Visualizing data: visualize data in a variety of ways. This can be used to make data more understandable and accessible.
Embedded analytics: embed analytics in websites and applications. This can be used to provide users with real-time insights into their data.

Looker is a powerful tool that to improve decision-making, operations, and gain a competitive advantage. If you are looking for an enterprise-grade business intelligence platform, then Looker is a good option to consider.

Benefits from modernization of services

Automation

Practice of managing and provisioning infrastructure through code instead of using manual processes. Improved reliability, agility, reduced cost. Main tools we employ include Terraform and Pulumi.

Scalability

Designed as easy to use and manage, scaled up or down as needed to meet demands. We ensure your applications are always available and can handle the most demanding workloads

Security

Data and applications are protected from a variety of threats. We implement controls and procedures following cloud best practices to ensure the security of your cloud environment.

Data Analytics

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William Bach Collection

Webb Hall

Past Exhibitions

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Most popular use cases for data analytics

Consumer insight

Fraud detection

Risk assesment

Compliance

Benefits from modernization of services

Automation

Scalability

Security

Providers and Services we use

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