Looking for something else? Check the categories of Storage:
Apache Avro Apache Cassandra Apache Hudi Apache Iceberg Apache Parquet Apache ZooKeeper Delta Lake Elasticsearch Embedded databases HDFS MySQL PostgreSQL Time series
If not, below you can find all articles belonging to Storage.
One of interesting data types used in Apache Cassandra are collections. In our model we can freely use maps, sets or lists.
Because tables in Apache Cassandra are very similar to the tables of relational databases, this article describing them won't focus on basic points. Instead, we'll explore more Cassandra specific subjects, such as configuration or different types.
Disk compaction helps to save space. Since Cassandra is supposed to store a lot of data, it can't miss this useful process.
Since Cassandra is distributed storage system, it holds data in different nodes. But how it determines data should be stored by each node ? It's the role of partitioners.
Keeping old data eternally takes place and makes reads longer. Apache Cassandra is not an exception and has a mechanism to remove data.
Previously we've presented theory of data consistency in Cassandra. Now it's a good moment to show some examples of consistency levels.
Distributed data brings a new problem to historical standalone relational databases - data consistency. Cassandra deals with this problem pretty nice with its different consistency levels.
Until now we're working with Cassandra without looking on what happens. It's a time to be a little bit more curious.
The previous article introduced us to Apache Cassandra by presenting vaguely its main concepts. This article focuses more in details on data topics.
After some articles about data ingestion and serialization in Big Data applications, it's time to start to learn about storage. This part begins with Apache Cassandra.
This article presents basic concepts of Apache Cassandra. In the first part it tries to explain architecture and general concepts of this solution. The second part is focused more on developer topics and it describes some main points about data organization.
A lot of programming tools implement event-driven approach. Apache ZooKeeper isn't an exception for this rule with its system of watchers.
Apache ZooKeeper is very often compared to distributed file system. Because each file system has a feature to deal with file permissions, ZooKeeper, as a kind of file system, can't be different.
Sometimes network latencies can slow down the communication between Apache ZooKeeper and its client. It's one of the reasons of possible use of asynchronous operations for zNodes manipulations.
Until now we've seen how to create zNodes. But creation is not the single thing that Apache ZooKeeper does.
Client connects to ZooKeeper server and maintains a session. There are several things to know about ZooKeeper sessions and we'll explore them in this article.
As already told, zNodes are a key part in Apache ZooKeeper. They store information shared among different servers directly (as binary data) or indirectly (as parent directories).
Usually Apache ZooKeeper works in the shadow of more exposed Big Data tools, as Apache Spark or Apache Kafka. However, its role is very important in system architecture.
At the begin Elastcisearch 2.2.0 was realeased on February 2016. Because my POC project was frozen with 1.6, I decided to upgrade. But not without surprises and some code rework.
After theoretical introduction to Apache Avro, we can see how it can be used.
Previously we learned why serialization frameworks can facilitate work in distributed systems, where data provide from several different sources. Now, it's a good time to discover some real tools used in serialization step. As told, the chosen tool is Apache Avro.
