refactoring repo

.gitignore

@@ -0,0 +1 @@
+.DS_Store

README.md

@@ -1,16 +1,5 @@
-# Designing Data-intensive applications
+# Book summary
+Repo to put ideas about books that I am reading
 
-An application is data-intensive if data is its primary challenge — the quantity of data, the complexity of data, or the speed at which it is changing — as opposed to compute-intensive , where CPU cycles are the bottleneck.
-
-As software engineers, we need to have a technically accurate and precise understanding of the various technologies and their trade-offs if we want to build good applications. Not just how they work, but also why they work that way, and what questions we need to ask.
-
-Behind the rapid changes in technology, there are enduring principles that remain true, no matter which version of a particular tool you are using.
-
-Building for scale that you don’t need is wasted effort and may lock you into an inflexible design. It is a form of premature optimization. However, it’s also important to choose the right tool for the job.
-
---- 
-## Foundations of Data Systems
-1. [Reliable, Scalable, and Maintainable Applications](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter01.md) 
-2. [Data models and Query languages](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter02.md) 
-3. [Storage and retrieval](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter03.md)
-4. [Encoding and evolution](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter04.md)
+- [Design data intensive application](designing-data-intensive-application/README.md)
+- [Effective Java](effective-java/README.md)

designing-data-intensive-application/README.md

@@ -0,0 +1,16 @@
+# Designing Data-intensive applications
+
+An application is data-intensive if data is its primary challenge — the quantity of data, the complexity of data, or the speed at which it is changing — as opposed to compute-intensive , where CPU cycles are the bottleneck.
+
+As software engineers, we need to have a technically accurate and precise understanding of the various technologies and their trade-offs if we want to build good applications. Not just how they work, but also why they work that way, and what questions we need to ask.
+
+Behind the rapid changes in technology, there are enduring principles that remain true, no matter which version of a particular tool you are using.
+
+Building for scale that you don’t need is wasted effort and may lock you into an inflexible design. It is a form of premature optimization. However, it’s also important to choose the right tool for the job.
+
+--- 
+## Foundations of Data Systems
+1. [Reliable, Scalable, and Maintainable Applications](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter01.md) 
+2. [Data models and Query languages](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter02.md) 
+3. [Storage and retrieval](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter03.md)
+4. [Encoding and evolution](https://github.com/iundarigun/designing-data-intensive-applications/blob/master/fundationsOfDataSystems/chapter04.md)