NATO on the Brink: Stoltenberg Reveals Behind-the-Scenes battles with Trump in New Book
Table of Contents
“This could be the meeting where NATO is in charge, I thought. It happens on my guard. It worked for 70 years. But not after July 12, 2018.”
That’s what Jens Stoltenberg writes in his new book “On My Guard”.
Over 427 pages, Stoltenberg openly recounts his ten years as Secretary General of NATO, with detailed descriptions of conversations behind closed doors during a turbulent time for the alliance.
Facts about “On My Guard”
* Full title: On my guard – to lead NATO in wartime
* Publisher: Gyldendal
* posted by Jens Stoltenberg in collaboration with Per anders Madsen.
at NATO’s two-day summit in Brussels on July 11-12,2018,things were going wrong.
“Everyone realized that it was about to collapse. The entire meeting,all the statements of agreement. And with that, NATO was in acute danger,” Stoltenberg writes.
The crisis was created by US president Donald Trump.
“Big fan”
When Trump won the US presidential election in 2016, Stoltenberg wanted NATO to quickly establish a good working relationship with him.
In the election campaign, trump had said that NATO was “obsolete” – the alliance had played its role.Now the goal was to make him more positive.
The first time Stoltenberg and Trump talked together was Friday, November 18, 2016. Trump then told him he was a “big big big fan” of NATO, writes Stoltenberg.
But Trump’s relationship with NATO quickly became more conflicting.
It centered on European defense budgets. European allies needed to start spending far more money on NATO.They could not continue to rely on US military superiority.
On Thursday, May 25, 2017, the Allies gathered for a summit in NATO’s new headquarters in Brussels. Stoltenberg writes that he was looking forward to it, but uneasy.
But Stoltenberg wanted it differently, and already in 2018 another summit was to be held.
The mood was more tense than ever.
Two months before the summit, on May 17, Trump welcomed Stoltenberg in the white House. Despite the Constitution Despite this meeting, Norway also got Norway.
“Can we kick them out? Can Norway have a Swedish model i
Trump threatened to pull the US out of NATO – and blamed Stoltenberg for the potential collapse
“The president of the United States was angry. He would no longer accept that Europe and Canada paid too little. Trump refused to agree,” Stoltenberg writes.
On the phone, according to Stoltenberg, Trump made it clear that the United States was not thriving NATO. “Look, If We Leave, We Leave”, he said.
“He pulled the United States out, the alliance was dead. Trump made me responsible for NATO being disintegrated. Now he wanted me to arrange before the summit. It was 12 more days.”
“Russia’s catch”
The very summit began on Wednesday,July 11,2018 with a breakfast between Trump and Stoltenberg in the US ambassador’s residence in Brussels.
While the press was still in the room, trump began to hammer. He was especially hard against Germany, which Trump called “Russia’s prisoner” as of the Germans’ great dependence on Russian gas.
“`html
What is a Data Lake? A Extensive Guide
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Unlike a data warehouse, which stores data in a pre-defined schema, a data lake stores data in its native format – think of it as a large pool of raw data. This adaptability is its biggest strength, but also introduces unique challenges.
Why Use a Data Lake?
Organizations are increasingly turning to data lakes to address the limitations of customary data storage solutions. Here’s why:
- Scalability: Data lakes can easily handle massive volumes of data, growing as your needs evolve.
- Cost-Effectiveness: Storing data in its raw format is generally cheaper than transforming and loading it into a data warehouse.
- Data Variety: Data lakes accommodate all types of data – structured, semi-structured, and unstructured – including logs, images, videos, and social media feeds.
- Agility: Data scientists and analysts can explore data without predefined schemas, enabling faster finding and innovation.
- Advanced Analytics: Data lakes are ideal for machine learning, predictive modeling, and other advanced analytics techniques.
Data Lake vs.Data Warehouse: Key Differences
While both data lakes and data warehouses are used for data storage and analysis,they serve different purposes. Here’s a quick comparison:
| Feature | Data Lake | Data Warehouse |
|---|---|---|
| Schema | schema-on-read (schema is applied when the data is read) | Schema-on-Write (schema is defined before data is loaded) |
| Data Types | Structured, Semi-structured, Unstructured | Primarily Structured |
| Purpose | Discovery, Advanced Analytics, Machine Learning | Reporting, Business Intelligence |
| Users | Data Scientists, Data Engineers | Business Analysts, Executives |
| Cost | Generally Lower | Generally Higher |
Key Components of a Data Lake
Building a triumphant data lake involves several key components:
- Data Ingestion: Tools and processes for bringing data into the lake from various sources.
- Data Storage: Typically utilizes object storage like Amazon S3, Azure Data Lake Storage, or Google Cloud Storage.
- Data Catalog: A metadata repository that provides data about the data in the lake, making it discoverable and understandable.
- Data Governance: Policies and procedures for ensuring data quality,security,and compliance.
- Data Processing: Engines like Spark, Hadoop, or Presto for transforming and analyzing data.
Challenges of Data Lakes
Despite their benefits, data lakes aren’t without challenges:
- Data Swamp: Without proper governance, a data lake can become a chaotic “data swamp” – difficult to navigate and extract value from.
- Security: Protecting sensitive data in a data lake requires robust security measures.
- data Quality: Ensuring data accuracy and consistency is crucial for reliable analytics.
- Complexity: building and managing a data lake can be complex, requiring specialized skills.
Best Practices for Data Lake Implementation
To avoid the pitfalls and maximize the benefits of a data lake,consider these best practices:
- Define Clear Use Cases: Start with specific business problems you want to solve.
- Implement Strong Data Governance: Establish policies for data quality, security, and access control.
- Invest in a Data Catalog: Make it easy for users to find and understand the data.
- Automate Data Ingestion and Processing: streamline the data pipeline.
- Monitor and Optimize Performance: Regularly assess and improve the data lake’s performance.
Frequently Asked Questions (FAQ)
- What is the difference between a data lake and a Hadoop