Notes about Cutting-Edge Technologies and Everything

Apache Pulsar's unique features such as tiered storage, stateless brokers, geo-aware replication, and multi-tenancy may be a reason to choose it over Apache Kafka. Today, many data architects, engineers, dev-ops, and business leaders are struggling to understand the pros and cons of Apache Pulsar and Apache Kafka. As someone who has worked with Kafka in the past, I wanted to compare these two technologies.  If you are looking for insights on when to use Pulsar, here are 10 advantages of the technology that might be the deciding factors for you. Continue reading >>>

With the v2 Compute-to-Data release, Ocean Protocol provides a means to exchange data while preserving privacy. This guide explains Compute-to-Data without requiring deep technical know-how. Private data is data that people or organizations keep to themselves. It can mean any personal, personally identifiable, medical, lifestyle, financial, sensitive or regulated information. Benefits of Private Data. Private data can help research, leading to life-altering innovations in science and technology. For example, more data improves the predictive accuracy of modern Artificial Intelligence (AI) models. Private data is often considered the most valuable data because it’s so hard to get at, and using it can lead to potentially big payoffs. Risks of Private Data. Sharing or selling private data comes with risk. What if you don’t get hired because of your private medical history? What if you are persecuted for private lifestyle choices? Large organizations that have massive datasets know their d...

Data produced by applications, devices, or humans must be processed before it is consumed. By definition, a data pipeline represents the flow of data between two or more systems. It is a set of instructions that determine how and when to move data between these systems. My last blog conveyed how connectivity is foundational to a data platform. In this blog, I will describe the different data processing pipelines that leverage different capabilities of the data platform, such as connectivity and data engines for processing. There are many data processing pipelines. One may: “Integrate” data from multiple sources Perform data quality checks or standardize data Apply data security-related transformations, which include masking, anonymizing, or encryption Match, merge, master, and do entity resolution Share data with partners and customers in the required format, such as HL7 Consumers or “targets” of data pipelines may include: Data warehouses like ...

Intro Recently I was put in charge of setting up a workflow for dealing with a large amount of raw DNA sequencing (well technically a SNP chip) data for my lab. The goal was to be able to quickly get data for a given genetic location (called a SNP) for use for modeling etc. Using vanilla R and AWK I was able to cleanup and organize the data in a natural way, massively speeding up the querying. It certainly wasn’t easy and it took lots of iterations. This post is meant to help others avoid some of the same mistakes and show what did eventually work. The Data The data was delivered to us by our university’s genetics processing center as 25 TB of tsvs. Before handing it off to me, my advisor split and gzipped these files into five batches each composed of roughly 240 four gigabyte files. Each row contained a data for a single SNP for a single person. There were ~2.5 million SNPS and ~60 thousand people Along with the SNP value there were multiple numeric columns on things like intensity o...

If you have been trying to harness the power of data science and machine learning — but, like many teams, struggling to produce results — there’s a secret you are missing out on. All of those models and sophisticated insights require lots of good data, and the best way to get good data quickly is by using DataOps. What is DataOps? It’s a way of thinking about how an organization deals with data. It’s a set of tools to automate processes and empower individuals. And it’s a new DataOps Engineer role designed to make that thinking real by managing and building those tools. DataOps Principles DataOps was inspired by DevOps, which brought the power of agile development to operations (infrastructure management and production deployment).  DevOps transformed the way that software development is done; and now DataOps is transforming the way that data management is done. For larger enterprises with a dedicated data engineering team, DataOps is about breaking down barriers and re-...

The story of Self-Service Data Preparation and academic research behind Trifacta, which is also a SaaS offering in GCP Dataprep:  http://sites.computer.org/debull/A18june/p23.pdf

In this post, I am going to discuss Apache Airflow, a workflow management system developed by Airbnb. Earlier I had discussed writing basic ETL pipelines in Bonobo. Bonobo is cool for write ETL pipelines but the world is not all about writing ETL pipelines to automate things. There are other use cases in which you have to perform tasks in a certain order once or periodically. For instance: Monitoring Cron jobs transferring data from one place to other. Automating your DevOps operations. Periodically fetching data from websites and update the database for your awesome price comparison system. Data processing for recommendation based systems. Machine Learning Pipelines. Possibilities are endless. Before we move on further to implement Airflow in our systems, let’s discuss what actually is Airflow and it’s terminologies. What is Airflow? From the Website: Airflow is a platform to programmatically author, schedule and monitor workflows....