Spanner has long been the backbone for Google’s most critical services, from Gmail and YouTube to Photos and Chat. Spanner is a one of a kind database. Some even say it defies the CAP Theorem. This is an engineering hero story on a new release for Spanner: Schema insights.
This feature, long used by Google engineers to optimize the applications millions use every day, is now available to help you harness Spanner’s superpowers with ease.
As applications grow, their access patterns naturally change. To ensure your database remains perfectly tuned to these evolving needs, Google Cloud engineers have made these internal optimization tools accessible to everyone. Schema Insights acts as a proactive guide right next to your tables in Spanner Studio, helping you maintain a schema that is always optimized for speed and scale.
Designing for High-Throughput Distribution
Spanner achieves horizontal scalability by partitioning and distributing data across different splits and servers. To ensure maximum performance, it is a best practice to distribute your workload evenly across these servers. Think of it as balancing the amount of data each server has, so they can all search and update at the same time.
Monotonic keys and hotspots
When a primary key uses a timestamp or an increasing integer, new records may land in the same partition. This can potentially create a “hotspot” as your applications access one partition predominantly more than others.
Insights will recommend strategies like hashing, bucketing, or bit-reversing key columns to ensure your data is distributed effectively for parallel processing.
Schema Insights highlighting monotonic keys
This is a common design pitfall that might not be obvious if we don’t have the distributed data storage aspect in mind. The good news is this can also be easily fixed. Consider these recommendations when designing the schema for your distributed database on Spanner.
Streamlining Schema Architecture
In a distributed database, the complexity of your schema directly impacts the performance of transactions and queries. Keeping the schema lean ensures that our application stays fast and maintainable.
A complex schema not only gets in the way of clean and maintainable code, it can make it harder to scale horizontally, increase latency and degrade performance of write and read operations. The insights below basically are designed to guide you through this process of designing and fine-tuning your schema the right way, and to avoid certain pitfalls that might not necessarily be obvious but lead to huge performance penalties.
Table density
While Spanner is fully relational, highly normalized schemas can increase memory and server overhead. A large number of tables may indicate a schema may need a redesign to apply patterns such as multitenancy.
Too many columns
This should be hard to reach and it probably means it’s time for a more holistic view of the schema design, but a table can have a maximum of 1024 columns. The issue will be highlighted when a table starts approaching that limit so we have time to react.
Relational integrity
Maintaining foreign key references requires additional data to be consulted during transactions, which can slow down processing. Optimize for transactional speed by balancing the number of foreign key references.
Unlock Deeper Observability
Schema Insights works within Spanner Studio so you have the schema and context handy. It also works in tandem with Spanner’s suite of observability tools.
For example, the Key Visualizer allows you to visualize your specific traffic patterns and see exactly how your schema choices affect data distribution. To further understand potential problematic queries, Query Insights will highlight issues like high CPU utilization.
The best part of these tools is they highlight your specific access patterns, so you can tailor your next steps based on your needs and knowledge of your applications.
One of the most common questions about secrets management strategies on Amazon Web Services (AWS) is whether an organization should centralize its secrets. Though this question is often focused on whether secrets should be centrally stored, there are four aspects of centralizing the secrets management process that need to be considered: creation, storage, rotation, and monitoring. In this post, we discuss the advantages and tradeoffs of centralizing or decentralizing each of these aspects of secrets management.
Centralized creation of secrets
When deciding whether to centralize secrets creation, you should consider how you already deploy infrastructure in the cloud. Modern DevOps practices have driven some organizations toward developer portals and internal developer platforms that use golden paths for infrastructure deployment. By using tools that use golden paths, developers can deploy infrastructure in a self-service model through infrastructure as code (IaC) while adhering to organizational standards.
A central function maintains these golden paths, such as a platform engineering team. Examples of services that can be used to maintain and define golden paths might include AWS services such as AWS Service Catalog or popular open source projects such as Backstage.io. Using this approach, developers can focus on application code while platform engineers focus on infrastructure deployment, security controls, and developer tooling. An example of a golden path might be a templatized implementation for a microservice that writes to a database.
For example, a golden path could define that a service or application must be built using the AWS Cloud Development Kit (AWS CDK), running on Amazon Elastic Container Service (Amazon ECS), and use AWS Secrets Manager to retrieve database credentials. The platform team could also build checks to help ensure that the secret’s resource policy only allows access to the role being used by the microservice and is encrypted with a customer managed key. This pattern abstracts deployments away from developers and facilitates resource deployment across accounts. This is one example of a centralized creation pattern, shown in Figure 1.
Figure 1: Architecture diagram highlighting the developer portal deployment pattern for centralized creation
The advantages of this approach are:
Consistent naming tagging, and access control: When secrets are created centrally, you can enforce a standard naming convention based on the account, workload, service, or data classification. This simplifies implementing scalable patterns like attribute-based access control (ABAC).
Least privilege checks in CI/CD pipelines: When you create secrets within the confines of IaC pipelines, you can use APIs such as the AWS IAM Access Analyzercheck-no-new-access API. Deployment pipelines can be templatized, so individual teams can take advantage of organizational standards while still owning deployment pipelines.
Create mechanisms for collaboration between platform engineering and security teams: Often, the shift towards golden paths and service catalogs is driven by a desire for a better developer experience and reduced operational overhead. A byproduct of this move is that security teams can partner with platform engineering teams to build security by default into these paths.
The tradeoffs of this approach are:
It takes time and effort to make this shift. You might not have the resources to invest in full-time platform engineering or DevOps teams. To centrally provision software and infrastructure like this, you must maintain libraries of golden paths that are appropriate for the use cases of your organization. Depending on the size of your organization, this might not be feasible.
Golden paths must keep up with the features of the services they support: If you’re using this pattern, and the service you’re relying on releases a new feature, your developers must wait for the features to be added to the affected golden paths.
In a decentralized model, application teams own the IaC templates and deployment mechanisms in their own accounts. Here, each team is operating independently, which can make it more difficult to enforce standards as code. We’ll refer to this pattern, shown in Figure 2, as a decentralized creation pattern.
Figure 2: Decentralized creation of secrets
The advantages of this approach are:
Speed: Developers can move quickly and have more autonomy because they own the creation process. Individual teams don’t have a dependency on a central function.
Lack of standardization: It can become more difficult to enforce naming and tagging conventions, because it’s not templatized and applied through central creation mechanisms. Access controls and resource policies might not be consistent across teams.
Developer attention: Developers must manage more of the underlying infrastructure and deployment pipelines.
Centralized storage of secrets
Some customers choose to store their secrets in a central account, and others choose to store secrets in the accounts in which their workloads live. Figure 3 shows the architecture for centralized storage of secrets.
Figure 3: Centralized storage of secrets
The advantages of centralizing the storage of secrets are:
Simplified monitoring and observability: Monitoring secrets can be simplified by keeping them in a single account and with a centralized team controlling them.
Some tradeoffs of centralizing the storage of secrets are:
Additional operational overhead: When sharing secrets across accounts, you must configure resource policies on each secret that is shared.
Additional cost of AWS KMS Customer Managed Keys: You must use AWS Key Management Service (AWS KMS) customer managed keys when sharing secrets across accounts. While this gives you an additional layer of access control over secret access, it will increase cost under the AWS KMS pricing. It will also add another policy that needs to be created and maintained.
High concentration of sensitive data: Having secrets in a central account can increase the number of resources affected in the event of inadvertent access or misconfiguration.
Account quotas: Before deciding on a centralized secret account, review the AWS service quotas to ensure you won’t hit quotas in your production environment.
Service managed secrets: When services such as Amazon Relational Database Service (Amazon RDS) or Amazon Redshift manage secrets on your behalf, these secrets are placed in the same account as the resource with which the secret is associated. To maintain a centralized storage of secrets while using service managed secrets, the resources would also have to be centralized.
Though there are advantages to centralizing secrets for monitoring and observability, many customers already rely on services such as AWS Security Hub, IAM Access Analyzer, AWS Config, and Amazon CloudWatch for cross-account observability. These services make it easier to create centralized views of secrets in a multi-account environment.
Decentralized storage of secrets
In a decentralized approach to storage, shown in in Figure 4, secrets live in the same accounts as the workload that needs access to them.
Figure 4: Decentralized storage of secrets
The advantages of decentralizing the storage of secrets are:
Account boundaries and logical segmentation: Account boundaries provide a natural segmentation between workloads in AWS. When operating in a distributed multi-account environment, you cannot access secrets from another account by default, and all cross-account access must be allowed by both a resource policy in the source account and an IAM policy in the destination account. You can use resource control polices to prevent the sharing of secrets across accounts.
AWS KMS key choice: If your secrets aren’t shared across accounts, then you have the choice to use AWS KMS customer managed keys or AWS managed keys to encrypt your secrets.
Delegate permissions management to application owners:When secrets are stored in accounts with the applications that need to consume them, application owners define fine-grained permissions in secrets resource policies.
There are a few tradeoffs to consider for this architecture:
Auditing and monitoring require cross-account deployments: Tools that are used to monitor the compliance and status of secrets need to operate across multiple accounts and present information in a single place. This is simplified by AWS native tools, which are described later in this post.
Automated remediation workflows: You can have detective controls in place to alert on any misconfiguration or security risks related to your secrets. For example, you can surface an alert when a secret is shared outside of your organizational boundary through a resource policy. These workflows can be more complex in a multi-account environment. However, we have samples that can help, such as the Automated Security Response on AWS solution.
Centralized rotation
Like the creation and storage of secrets, organizations take different approaches to centralizing the lifecycle management and rotation of secrets.
When you centralize lifecycle management, as shown in Figure 5, a central team manages and owns AWS Lambda functions for rotation. The advantages of centralizing the lifecycle management of secrets are:
Developers can reuse rotation functions: In this pattern, a centralized team maintains a common library of rotation functions for different use cases. An example of this can be seen in this AWS re:Inforce session. Using this method, application teams don’t have to build their own custom rotation functions and can benefit from common architectural decisions regarding databases and third-party software as a service (SaaS) applications.
Logging: When storing and accessing rotation function logs, the centralized pattern can simplify managing logs from a single place.
Figure 5: Centralized rotation of secrets
There are some tradeoffs in centralizing the lifecycle management and rotation of secrets:
Additional cross-account access scenarios: When centralizing lifecycle management, the Lambda functions in central accounts require permissions to create, update, delete and read secrets in the application accounts. This increases the operational overhead required to enable secret rotation.
Service quotas: When you centralize a function at scale, service quotas can come into play. Check the Lambda service quotas to verify that you won’t hit quotas in your production environments.
Decentralized rotation
Decentralizing the lifecycle management of secrets is a more common choice, where the rotation functions live in the same account as the associated secret, as shown in Figure 6.
Figure 6: Decentralized rotation of secrets
The advantages of decentralizing the lifecycle management of secrets are:
Templatization and customization: Developers can reuse rotation templates, but tweak the functions as needed to meet their needs and use cases
No cross-account access: Decentralized rotation of secrets happens all in one account and doesn’t require cross-account access.
The primary tradeoff of decentralizing rotation is that you will need to provide either centralized or federated access to logs for rotation functions in different accounts. By default, Lambda automatically captures logs for all function invocations and sends them to CloudWatch Logs. CloudWatch Logs offers a few different ways that you can centralize your logs, with the tradeoffs of each described in the documentation.
Centralized auditing and monitoring of secrets
Regardless of the model chosen for creation, storage, and rotation of secrets, centralize the compliance and auditing aspect when operating in a multi-account environment. You can use AWS Security Hub CSPM through its integration with AWS Organizations to centralize:
In this scenario, shown in Figure 7, centralized functions get visibility across the organization and individual teams can view their posture at an account level with no need to look at the state of the entire organization.
For organizations that don’t require centralized auditing and monitoring of secrets, you can configure access so that individual teams can determine which logs are collected, alerts are enabled, and checks are in place in relation to your secrets. The advantages of this approach are:
Flexibility: Development teams have the freedom to choose what monitoring, auditing, and logging tools work best for them.
Reduced dependencies: Development teams don’t have to rely on centralized functions for alerting and monitoring capabilities.
The tradeoffs of this approach are:
Operational overhead: This can create redundant work for teams looking to accomplish similar goals.
Difficulty aggregating logs in cross-account investigations: If logs, alerts, and monitoring capabilities are decentralized, it can increase the difficulty of investigating events that affect multiple accounts.
Putting it all together
Most organizations choose a combination of these approaches to meet their needs. An example is a financial services company that has a central security team, operates across hundreds of AWS accounts, and has hundreds of applications that are isolated at the account level. This customer could:
Centralize the creation process, enforcing organizational standards for naming, tagging, and access control
Decentralize storage of secrets, using the AWS account as a natural boundary for access and storing the secret in the account where the workload is operating, delegating control to application owners
Decentralize lifecycle management so that application owners can manage their own rotation functions
Centralize auditing, using tools like AWS Config, Security Hub, and IAM Access Analyzer to give the central security team insight into the posture of their secrets while letting application owners retain control
Conclusion
In this post, we’ve examined the architectural decisions organizations face when implementing secrets management on AWS: creation, storage, rotation, and monitoring. Each approach—whether centralized or decentralized—offers distinct advantages and tradeoffs that should align with your organization’s security requirements, operational model, and scale. The important points include:
Choose your secrets management architecture based on your organization’s specific requirements and capabilities. There’s no one solution that will fit every situation.
Use automation and IaC to enforce consistent security controls, regardless of your approach.
Implement comprehensive monitoring and auditing capabilities through AWS services to maintain visibility across your environment.
Resources
To learn more about AWS Secrets Manager, check out some of these resources:
Brendan Paul
Brendan is a Senior Security Solutions Architect at AWS and has been at AWS for more than 6 years. He spends most of his time at work helping customers solve problems in the data protection and workload identity domains. Outside of work, he’s pursuing his master’s degree in data science from UC Berkeley.
Eduardo Patroncinio
Eduardo is a distinguished Principal Solutions Architect on the AWS Strategic Accounts team, bringing unparalleled expertise to the forefront of cloud technology. With an impressive career spanning more than 25 years, Eduardo has been a driving force in designing and delivering innovative customer solutions within the dynamic realms of cloud and service management.
AWS Security Agent now supports GitHub Enterprise Cloud, enabling customers to connect their GitHub Enterprise Organization and leverage AI-powered security capabilities across their private repositories. With this expansion, development teams can integrate security analysis directly into their GitHub workflows.
Customers can now connect their GitHub Enterprise Organization to AWS Security Agent by installing the AWS Security Agent GitHub app with the required permissions. Once connected, the agent provides three key capabilities for private repositories:
Automated Code Reviews: AWS Security Agent performs comprehensive security reviews on new pull requests, identifying vulnerabilities and compliance with internal security requirements before code is merged.
Penetration Testing Integration: Leverage your GitHub Enterprise code repositories during penetration testing activities, allowing the agent to analyze your codebase for potential security weaknesses and attack vectors.
Automated Code Remediation: When security issues are identified during penetration testing, customers can choose to have AWS Security Agent automatically submit pull requests with recommended fixes, accelerating remediation workflows.
This capability is available in the US East (N. Virginia) region where AWS Security Agent operates. To get started, connect your GitHub Enterprise Organization to AWS Security Agent through the AWS Security Agent console. To learn more about AWS Security Agent, visit the product page.
PDF files are everywhere. They’re used for reports, invoices, bank statements, research papers, and legal documents. While PDFs are easy to read for humans, they’re not easy to work with in code. Extracting text, splitting pages, or merging files often feels harder than it should be.
This is where PyPDF helps. PyPDF is a popular Python library that lets you read, modify, and write PDF files. It’s lightweight, easy to learn, and works well for most common PDF tasks. If you have ever needed to extract text from a PDF, merge multiple PDFs, or protect a file with a password, PyPDF is a good place to start.
In this article, you’ll learn what PyPDF is, how it works, and how to use it through simple and practical examples. You’ll also learn how tools like PDFBoom handle PDF operations. This tutorial requires a basic understanding of Python.
PyPDF is a pure Python library for working with PDF files. It allows you to open existing PDFs, read their structure, extract content, and create new PDF files. Since it’s written in Python, it doesn’t require external tools or system-level dependencies.
The library understands the internal layout of a PDF, such as pages, text streams, metadata, and encryption. You don’t need to know how PDFs work internally to use PyPDF, but it helps to understand that a PDF is not just text. It’s a structured document with objects and references.
PyPDF is often used in automation scripts, data pipelines, compliance systems, and document processing tools. It’s a common choice when you need a reliable and simple solution without heavy dependencies.
Installing PyPDF
Installing PyPDF is straightforward. You can install it using pip, which is the standard package manager for Python.
pip install pypdf
Once installed, you can import it in your Python code. The main class you’ll use is PdfReader for reading files and PdfWriter for creating or modifying them.
from pypdf import PdfReader, PdfWriter
With this setup, you’re ready to start working with PDFs.
Reading a PDF File
The first step in most tasks is opening a PDF file. PyPDF makes this simple using the PdfReader class.
from pypdf import PdfReader
reader = PdfReader("sample.pdf")
print(len(reader.pages))
This code opens a PDF file and prints the number of pages. Each page in the document is represented as an object that you can access and work with.
You can also inspect basic metadata such as the title or author if it’s available.
metadata = reader.metadata
print(metadata)
Metadata is optional in PDFs, so not all files will contain meaningful values.
Extracting Text from a PDF
One of the most common use cases is text extraction. PyPDF allows you to extract text page by page.
from pypdf import PdfReader
reader = PdfReader("sample.pdf")
page = reader.pages[0]
text = page.extract_text()
print(text)
This code extracts text from the first page of the PDF. For many documents like reports or articles, this works well.
It’s important to understand that text extraction isn’t perfect. PDFs store text based on layout, not reading order. This means extracted text may appear out of order or missing spaces in some cases. Still, for most structured documents, PyPDF provides usable results.
If you want to extract text from all pages, you can loop through them.
full_text = ""
for page in reader.pages:
full_text += page.extract_text() + "\n"
print(full_text)
This approach is common when building search indexes or document analysis pipelines.
Splitting a PDF into Multiple Files
Another practical task is splitting a PDF into smaller files. This is useful when dealing with large reports or scanned documents.
from pypdf import PdfReader, PdfWriter
reader = PdfReader("sample.pdf")
for i, page in enumerate(reader.pages):
writer = PdfWriter()
writer.add_page(page)
with open(f"page_{i + 1}.pdf", "wb") as f:
writer.write(f)
This code creates one PDF file per page. Each new file contains exactly one page from the original document.
You can also split a PDF into chunks, such as every five pages, by controlling how many pages you add before writing the file.
Merging Multiple PDFs
Merging PDFs is another common requirement. PyPDF allows you to combine several PDF files into one.
from pypdf import PdfReader, PdfWriter
writer = PdfWriter()
files = ["file1.pdf", "file2.pdf", "file3.pdf"]
for file in files:
reader = PdfReader(file)
for page in reader.pages:
writer.add_page(page)
with open("merged.pdf", "wb") as f:
writer.write(f)
This script reads each input file and appends all pages to a single output PDF. The order of files in the list defines the order in the merged document.
This approach is often used in reporting systems where multiple outputs are combined into one final document.
Rotating and Modifying Pages
Sometimes you need to rotate pages, especially when working with scanned documents. PyPDF makes this easy.
from pypdf import PdfReader, PdfWriter
reader = PdfReader("sample.pdf")
writer = PdfWriter()
page = reader.pages[0]
page.rotate(90)
writer.add_page(page)
with open("rotated.pdf", "wb") as f:
writer.write(f)
This code rotates the first page by 90 degrees clockwise. You can apply similar logic to all pages or selected pages.
You can also crop pages by adjusting their media box, which controls page dimensions. This is useful when removing margins or focusing on a specific area.
Encrypting and Decrypting PDFs
Security is important when handling sensitive documents. PyPDF supports PDF encryption and password protection.
from pypdf import PdfReader, PdfWriter
reader = PdfReader("sample.pdf")
writer = PdfWriter()
for page in reader.pages:
writer.add_page(page)
writer.encrypt("strongpassword")
with open("protected.pdf", "wb") as f:
writer.write(f)
The resulting PDF requires a password to open. This is commonly used for sharing confidential reports or financial documents.
If you need to read an encrypted PDF, you can provide the password when opening it.
After decryption, you can work with the file like any other PDF.
Adding Metadata to a PDF
Metadata helps describe a document. You can add or update metadata using PyPDF.
from pypdf import PdfWriter
writer = PdfWriter()
writer.add_metadata({
"/Title": "Monthly Report",
"/Author": "Finance Team",
"/Subject": "Revenue Analysis"
})
with open("metadata.pdf", "wb") as f:
writer.write(f)
This metadata becomes part of the PDF file and can be viewed in most PDF readers. It is useful for document management and search systems.
Common Limitations of PyPDF
While PyPDF is powerful, it has limitations. It doesn’t perform optical character recognition (OCR), so it can’t extract text from scanned images. For scanned PDFs, you need OCR tools like Tesseract.
Complex layouts such as multi-column text or tables may not extract cleanly. In such cases, post-processing or layout-aware tools are needed.
Despite these limits, PyPDF is reliable for a large range of everyday PDF tasks.
When to Use PyPDF
PyPDF is best suited for automation, backend services, and scripts where you need simple and fast PDF processing. It works well in data pipelines, compliance systems, and internal tools.
If you need advanced layout understanding or visual analysis, you may need heavier tools. But for most developers, PyPDF covers the core needs with minimal effort.
Conclusion
PyPDF is a practical and easy-to-use library for working with PDF files in Python. It allows you to read documents, extract text, merge and split files, rotate pages, and add security with just a few lines of code.
Its simple API and lightweight design make it a strong choice for developers who want to automate PDF workflows without extra complexity. While it doesn’t solve every PDF problem, it handles the most common ones very well.
If you work with PDFs regularly, learning PyPDF is a valuable skill that can save time and reduce manual work across many projects.
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La compañía de Elon Musk lleva casi un mes ignorando la medida cautelar acordada por la jueza en el proceso judicial iniciado por ACO con el fin de “evitar que continúe cometiendo delitos de odio” en esta red
etiquetas: libertad de expresión, discurso de odio, justicia, racismo
En este vídeo vivimos desde dentro la organización de El Salón del Videojuego, un evento celebrado en Madrid a principios de febrero, mostrando todo lo que no se ve cuando visitas una feria de videojuegos como público. Acompañamos a José, organizador del evento y responsable de Game Press, durante los días de montaje y celebración, para entender qué implica realmente levantar una feria de este tamaño en solo tres meses, sin ayudas institucionales y con un riesgo económico muy alto.
etiquetas: feria, evento, salón del videojuego, madrid, coste, organización
Los argumentos se basaban en una entrevista que José Luis Ábalos llevó a cabo en OkDiario en la que aseguraba que el presidente le avisó de la investigación en su contra antes de que se hiciera pública
etiquetas: vox, pedro sánchez, tribunal supremo, querella
Brendan Paul
Eduardo Patroncinio
