If you google the word ‘ snowflake ‘ for the first time, you will likely get references to the ‘ it insult ‘ word, Taiwanese dessert, a crystal of snow, etc. Once you find that Snowflake is the hot new data warehouse in the cloud, your subsequent searches are likely to be more productive in terms of digging up documentation . However, complex topics such as “ Snowflake database recovery ” have likely not made it into Stackoverflow or your favorite internet group yet. In t

As part of our Analytics practice, we repeatedly encounter use cases where complex calculations over large datasets are required. In this series, we will compare two distributed computing systems – Dask and Spark – that have become very popular in recent times. To evaluate these, we will select a use case that comes up in many industries – calculating cross correlations across large datasets. This has several applications in healthcare (correlational study of outcomes, gene e

At Data-Aces, we provide managed services running Cassandra for our customers. With more and more attention being given to data security these days, end-to-end encryption of ALL communication is becoming a mandatory requirement. This includes traffic between the Cassandra nodes or from client to the Cassandra cluster. This means, cqlsh with SSL is also a requirement. The default config file (Cassandra.yaml) is configured for normal, unencrypted communication between clients a

TensorFlow supports running computations on a variety of types of devices, including CPU and GPU. Originally used for display functions, GPUs were developed to scale up parallel computations using thousands of cores. This happens to match very well with how we execute the training of Machine Learning algorithms in Deep Learning Models. In python, the base ‘tensorflow’ package is for CPU only; a separate package, tensorflow-gpu is used when we plan to run on GPUs. So why did