Big Data problems - solutions articles


Change Data Capture and NoSQL

Change Data Capture (CDC) is a technique helping to smoothly pass from classical and static data warehouse solution to modern streaming-centric architecture. To do that you can use solutions like Debezium which connects RDBMS or MongoDB to Apache Kafka. In this post, I will try to check whether CDC can also apply to other data stores like Apache Cassandra, Elasticsearch and AWS DynamoDB. Continue Reading →

Batch layer in streaming-based architectures - approaches

Streaming processing is great because it guarantees low latency and quite fresh insight. But on the other side, we won't always need such latency and for these situations, a batch processing will often be a better fit because of apparently simpler semantics. In data architectures, batch layer is perceived differently. Kappa, which is a streaming-based model, makes it optional when the streaming broker can guarantee long data retention. But if it's not the case, the data must be copied into some more persistent storage like a distributed file system. Continue Reading →

Immutability and key-value storage

The immutability is a precious property of systems dealing with a lot of data. It's especially true when something goes wrong and we must recover quickly. Since the data is immutable, the cleaning step is not executed and with some additional computation power, the data can be regenerated efficiently. Continue Reading →

Index table pattern in NoSQL

Good write throughput and horizontal scalability are maybe the most visible advantages of NoSQL storage systems. However very often people with a solid RDBMS background fall in the trap of index that can't be so easily created. Fortunately, a lot of patterns helping to deal with this problem exist. One of them is the index table pattern. Continue Reading →