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This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:
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NEW QUESTION # 329
You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?
Answer: C
NEW QUESTION # 330
You have a variety of files in Cloud Storage that your data science team wants to use in their models. Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?
Answer: A
NEW QUESTION # 331
Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?
Answer: B
Explanation:
To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:
* Data Mesh Architecture:
* Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.
* Dataplex Lakes and Zones:
* Lakesin Dataplex are logical containers for managing data at scale, andzonesare subdivisions within lakes for organizing data based on domains, teams, or other criteria.
* Domain and Team Management:
* By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.
* This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.
Implementation Steps:
* Create Lakes and Zones:
* Create separate lakes in Dataplex for each team (analytics and data science).
* Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).
* Attach BigQuery Datasets:
* Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.
* Decentralized Management:
* Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.
Reference Links:
* Dataplex Documentation
* BigQuery Documentation
* Data Mesh Principles
NEW QUESTION # 332
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of
their loads
Perform analytics on all their orders and shipment logs, which contain both structured and unstructured
data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
- 8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
- 3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs
- 60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
Network-attached storage (NAS) image storage, logs, backups
10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.
Aggregate data in a centralized Data Lake for analysis
Use historical data to perform predictive analytics on future shipments
Accurately track every shipment worldwide using proprietary technology
Improve business agility and speed of innovation through rapid provisioning of new resources
Analyze and optimize architecture for performance in the cloud
Migrate fully to the cloud if all other requirements are met
Technical Requirements
Handle both streaming and batch data
Migrate existing Hadoop workloads
Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability.
Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
Answer: A
NEW QUESTION # 333
You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?
Answer: A
Explanation:
To securely connect multiple applications with dynamic public IP addresses to a Cloud SQL instance using public IP, the Cloud SQL Auth proxy is the best solution. This proxy provides secure, authorized connections to Cloud SQL instances without the need to configure authorized networks or deal with IP whitelisting complexities.
* Cloud SQL Auth Proxy:
* The Cloud SQL Auth proxy provides secure, encrypted connections to Cloud SQL.
* It uses IAM permissions and SSL to authenticate and encrypt the connection, ensuring data security in transit.
* By using the proxy, you avoid the need to constantly update authorized networks as the proxy handles dynamic IP addresses seamlessly.
* Authorized Network Configuration:
* Leaving the authorized network empty means no IP addresses are explicitly whitelisted, relying solely on the Auth proxy for secure connections.
* This approach simplifies network management and enhances security by not exposing the Cloud SQL instance to public IP ranges.
* Dynamic IP Handling:
* Applications with dynamic IP addresses can securely connect through the proxy without the need to modify authorized networks.
* The proxy authenticates connections using IAM, making it ideal for environments where application IPs change frequently.
Google Data Engineer References:
* Using Cloud SQL Auth Proxy
* Cloud SQL Security Overview
* Setting up the Cloud SQL Auth Proxy
By using the Cloud SQL Auth proxy, you ensure secure, authorized connections for applications with dynamic public IPs without the need for complex network configurations.
NEW QUESTION # 334
......
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