I am helping a small-mid sized company build their data analytics/warehouse platform. The data sources are micro-services around business applications recording transactional data, and the analytics platform should support historical reporting/aggregations/ad-hoc querying capabilities with quick response times and ability for data science folks to play with raw data.

There has been a lot of churn/improvements in architecture and tools used in recent times. What would one use in 2018 or how would one navigate the plethora of choices that exists at each layer(data ingest, transformation, data lake, reporting db, business intelligence tool)?