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I wrote The Security Data Lake in 2015. At the time, the big data space was not as mature as today — and the intersection of big data and security wasn’t a well understood area. Fast forward to today, people are talking about the data lakehouse. It is a new concept made possible by new database technologies, projects, and companies pushing the envelope to solve our modern data management and analytics challenges. Or said differently, they are all trying to make our data actionable at the lowest possible cost.
In this first-of-three post, I am going to look at what happened in the big data world during the past few years. In the second blog post, we’ll explore what a data lakehouse is and we will look around to understand some of the latest big data projects and tools that promise to uncover the secrets hidden in our data.
Let me start with a bit of a rant about database technologies. Back in the day, we had relational databases: the MySQLs and Oracles of the world. And the world was good. Then we realized that not all data and not all access patterns were suited for these databases, so we invented the document stores, the search engines, the graph databases, the key value stores, the columnar databases, etc. And that’s when life got complicated. What database do you use for what purposes? Often it seemed like we’d need multiple ones. But that would have meant we’d needed to duplicate data, pick the right database for the task at hand, synchronize the data, etc. A nightmare. What happened then was that we just started using the technology that seemed to cover most of our needs and abused it for the other tasks. I have seen one too many document stores used to serve complex analytical questions (i.e., asking Lucene to return aggregate metrics and ad-hoc summaries).
Alongside the database technologies themselves, there is a notable secondary trend: increased requirements from a regulatory, privacy, and data locality perspective. Regulations like GDPR are imposing restrictions and requirements on how data can be stored and give individuals the right to see their data and even modify or delete it upon request. Some data stores have come up with privacy features, which are often in harsh contradiction to the insights we are looking for in the data. Finally, with increasingly going global, it matters where we collect and process our data. Not just for privacy purposes, but rather for processing speed and storage requirements. How, for example, do you compute global summaries over your data? Do you bring the data into one data center? Or do you compute local aggregates to then summarize them? Latency and storage costs are important factors to consider.
Wouldn’t it be nice if we had a data system that took care of all the above mentioned requirements automatically? It ingests the data we send to it – structured, unstructured, sensitive, non sensitive, anything. And on the other side, we formulate queries (I think we should keep SQL as the lingua franca for this) to answer the questions we have. Of course, we can add nice visualization layers on top, but that’s icing on the cake. I’d love a self-adjusting system. Don’t make me choose whether I wanted a graph database or not. Don’t make me configure data localities or privacy parameters. Let the system determine the necessary parameters – maybe bring me in the loop for things that the system cannot figure out itself, but make it easy on me. Definitely don’t ask me to create indexes or views. Let the system figure out those properties on the fly, while observing my access patterns. Move the data to where it is needed, create summary tables and materialized views transparently, while keeping storage cost and regulatory constraints in mind.
Now that we talked about storage and access, what about ETL? The challenge with translating data on ingest is that the translation often means loss of information. On the flip side, it makes analytics tasks easier and it helps clean the data. Take security logs (syslog), for example. We could store them in their original form as an unstructured string, or we could parse out every element to store the individual fields in a structured way. The challenge is the parser. If we get things wrong, we will loose entire log records. If, however, we stored the logs in their original form, we could do the transformation (parsing) at the time of analytics. The drawback then being that we will parse the same data multiple times over; every time we query or run any analytics on it. What to do? Again, wouldn’t it be nice if the data system took care of this decision for us? Keep the original data around if necessary, parse where needed, re-parse on error, etc.
Let’s look at one final piece of the data system puzzle, analytics. With the advent of cloud, there has been a big push to centralize analytics. That means all the data has to be shipped to a single, central location. That in itself is not always cheap, nor fast. We need an approach that allows us to keep some data completely decentralized. Leave the data at the place of generation and use the compute there to derive partial answer. Only send around the data that is needed. Again, with all the constraints and requirements we might have, such as compute availability and cost, hybrid data storage, considerations of fail over, redundancy, backups, etc. And again, I don’t want to configure these things. I’d like the system to take care of them after I told it some guiding parameters.
I will explore what has happened in the last couple of years in the big data ecosystem and what the lakehouse is about. Is there maybe a solution out there that sufficiently satisfies the above requirements?
This story originally appeared on Raffy.ch. Copyright 2021
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