AWS re:Invent — Niners share their experience

1. Levi9: a proud AWS partner

Back in 2016, a few enthusiastic people from Levi9 decided to start our journey towards AWS partnership. With no certified people at that moment and practically just a bit of experience with AWS it was an unexpected journey. Moving forward to 2022, more than 120 certified people2 AWS competences, 1 partnership program and soon to become Premier Tier partners. We almost succeeded in all, however our ambition is to achieve even more.

2. AWS re:Invent — keynote speeches recap

3. Learning from AWS partners @AWS re:Invent

AWS gives a lot of attention to their partners and during the conference we could hear many great stories and achievements from many companies. Even more, there was a keynote dedicated to partners where many partners had a chance to present their solutions. Most impressive was financial company from Brazil with 10 million accesses per day, 2500 microservices, 1 million API calls per minute which concentrates 10% of all payments in Brazil. They managed to scale from 100.000 customers to more than 20 million customers in just five years! Their example taught us that re-engineering of complete platform and moving it to the “elastic” cloud environment gives a great opportunity for large scaleups. AWS Certified people were also recognized during the whole conference. We’ve managed to meet many inspiring people, some of them having all 12 AWS certificates, which is definitely a great success.

4. AWS re:Invent takeaway — Levi9 is on the right track

By meeting some of the great attendees as well as AWS employees, we’ve realized that we as Levi9 are doing great things and moving in the right direction. It was an amazing experience to compare us to some of the biggest AWS customers and partners. Even though we aren’t the biggest partners compared to all the giants that were there, nor the biggest customer of AWS, our strategy and our goals are leading us into a bright future. So, who knows, with enthusiastic Levi9 people and great energy, we might become one of those giants in the future. 😊

After all, re:Invent is a great place to be. With all the sessions that you can learn from, opportunities to meet experts from all over the world, it is also a nice place to have a bit of fun as well.

Data Lake as an answer — The evolution, standards and future driving force

Data Lake as an answer — The evolution, standards and future driving force

Aleksander Bircakovic, Data Teach Lead @ Levi9

Designing a Data Lake: cloud or on-prem system?

Efficiency and scalability

Assessment of the current needs and prediction of potential growth can be a challenging task. When talking about on-prem system, it is necessary to assess the current needs as well as the potential growth in the upcoming period in order to put together a business justification for securing the funds.
On the other hand, Cloud Platforms usually charge for services based on used or reserved processing power and used storage, and with this billing model, they enable quick start of the journey towards an MVP solutions. As the complexity of requirements increases as well as the amount of data, the Cloud platform system can be easily scaled up. Storing data in the form of blobs is usually very cheap and practically unlimited. Database servers can be scaled as needed with the allocation of stronger instances, while processing power in the form of code packaged in containers or distributed systems that are terminated after the work is done is charged according to the used processing power and other resources. Tools like AWS Glue, Google DataFlow, AWS Cloud Functions etc. are just some of the options that offer those capabilities.

Data Catalog and service integration

Reliability, maintenance, and security

  • free disk space,
  • processor and memory allocation,
  • sharing of hardware resources with other applications (shared & noisy hardware) and users,
  • failure of one node in the cluster and redistribution of the topics to another or,
  • in a slightly more extreme case, the termination of the master node.

Cost optimization

AWS, GCP or Azure? Similar concepts, different skin

Data lake and lake-house

Data lake or data mesh? Technological or organizational dilemma?

Data lake layers

Solution as a service — Databricks