Data Management

is a process of collecting, organizing, storing, and managing data in a way that makes it useful and accessible. It is a critical function for any organization that wants to make effective use of its data. Developing and implementing data management policies and procedures helps to ensure that data is accurate, secure, and accessible. It is a powerful tool that can help businesses to improve their operations, make better decisions, as well as gain a competitive advantage.

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Lakehouse is a data architecture that combines the benefits of a data lake with the ease of use of a data warehouse. Lakehouses use a unified data model to store all your data. They provide a variety of tools and services for managing and querying data. This makes lakehouses ideal for businesses that need to store and analyze large amounts of data from a variety of sources.

Here are some of the key differences between data lakes and lakehouses:

Data storage: data lakes are designed to store all your data, regardless of its origin or format. This can include structured, semi-structured, and unstructured data. Lakehouses also support a variety of data formats, but they typically focus on storing structured data.
Data management: data lakes have scalable and flexible design, but can be difficult to manage and query. Lakehouses use a unified data model to store all your data, and provide a variety of tools and services for managing and querying your data.
Data analysis: Data lakes can be used for a variety of data analysis tasks, but may not be suitable for all kinds of data analysis. Lakehouses are designed for data analysis providing a variety of tools and services for performing data analysis tasks.

Overall, lakehouses offer a number of advantages over data lakes, including:

Ease of use: lakehouses are easier to use than data lakes, as they provide a unified data model and a variety of tools and services for managing and querying your data.
Performance: lakehouses can provide better performance than data lakes, as they are designed for data analysis.
Cost: lakehouses can be more cost-effective than data lakes, as they do not require you to purchase and maintain separate systems for data storage and data analysis.

If you are looking for a data architecture that is easy to use, provides good performance, and is cost-effective, then a lakehouse is a good option for you.

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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 management 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 management. 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 management 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 management. 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 management 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 management. 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 management needs.
Cost-effectiveness: BigQuery is a cost-effective solution for data management. 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 management 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 management 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 management. 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 management 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 management. 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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Dec 2023

Data catalog is a centralized repository of information about data assets. It can help organizations to discover, understand, and manage their data more effectively. Data catalogs can provide a variety of features, including:

Data discovery: Data catalogs can help organizations to find the data they need by providing a searchable index of all data assets.
Data lineage: Data catalogs can help organizations to understand the lineage of their data by tracking the origin and destination of data.
Data quality: Data catalogs can help organizations to assess the quality of their data by providing metrics on data completeness, accuracy, and consistency.
Data governance: Data catalogs can help organizations to implement data governance policies by providing a way to track data access and usage.

Data catalogs can be a valuable tool for data management, helping organizations to improve data discovery, understanding, and governance. This can lead to improved decision-making, increased productivity, and reduced risk.

Here are some of the benefits of using a data catalog:

Improved data discovery: Data catalogs make it easier for users to find the data they need. This can save time and improve productivity.
Increased data understanding: Data catalogs provide information about the data, such as its lineage, quality, and usage. This can help users to understand the data and use it more effectively.
Enhanced data governance: Data catalogs can help organizations to implement data governance policies. This can help to ensure that data is used appropriately and that its quality is maintained.

Overall, data catalogs can be a valuable tool for data management to improve data discovery, understanding, and governance leading to improved decision-making, increased productivity, and reduced risk.

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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 management 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 management. 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 management 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 management. 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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Cloud Composer is a fully managed service that helps you building and managing Apache Airflow pipelines.

Apache Airflow is a workflow orchestration tool that can be used to automate data pipelines.

Cloud Composer can be used for a variety of data management tasks, including:

Data ingestion: ingest data from a variety of sources, including cloud storage, databases, and streaming data sources.
Data transformation: transform data using a variety of tools, including SQL, Python, and Java.
Data loading: load data into a variety of destinations, including cloud storage, databases, and machine learning models.
Data governance: govern data using a variety of features, including data lineage, data quality, and data access control.

Cloud Composer can be a valuable tool for data management, helping to improve 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 Composer:

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 management workloads.
Cost-effectiveness: a cost-effective solution for data management. You only pay for the resources you use.

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

Here are some of the limitations of Cloud Composer:

*Cloud Composer 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 Composer can be more expensive than self-managed solutions if you only need to manage small amounts of data.
*Cloud Composer can be more complex to use than self-managed solutions if you need to customize your data management pipelines.

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

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 Management

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Past Exhibitions

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Most popular use cases for data management

Warehousing

Processing

Governance

Visualization

Benefits from modernization of services

Automation

Scalability

Security

Providers and Services we use

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