Aketch  Rachel

Aketch Rachel


Building Data Platforms — The ETL bias

This is the first article of the Building Data Platforms series. Why doing this in the first place you might be asking? It is simple. If you step back and look at the way the software industry approaches Data you see it has not fundamentally changed if we compare to other shifts that happened in the last 10–15 years. We got new tools such as Kafka, Spark, Flink and Snowflake but we haven’t changed the mindset of how we build Data Platforms. As you will see, this has a lot of consequences that go from lack of productivity, broken systems, knowledge silos, unhappy people, etc.

Let’s start this series with what I consider to be the biggest problem in building Data Platforms — the ETL bias.

What is ETL?

ETL stands for Extract, Transform and Load and became quite popular in 1970s. During that decade, companies started to have multiple data repositories and wanted to persist relevant information in one place for analysis. ETL became the de facto standard to perform these type of actions.


This step is responsible for reading data from a set of Data sources. These usually are relational databases, NoSQL databases, JSON, CVS or XML files, etc. Most of the times, this step requires direct read only access to where the data is located.


Translates the source data to match the format of the destination system. Operations in this stage include changing data types, combining or splitting fields, applying more complex formulas to derive new fields.


This steps takes the Data that was resulted from the Transform stage and persists it into a target data system. It is very common for the target system to be a Data Warehouse or in a file system.

ETLs can be chained together to create pipelines of computations and are usually scheduled to run with a given frequency (e.g. hourly, daily, monthly, etc).

Now that we have a definition of what ETL is, let’s start to understand some of the problems it has.

The first Data Product

The first Data Product in a company is usually the same and answers the following need

“We need metrics and KPIs about our main product”.

It is important to mention that we are not talking about Platforms yet but a simple Data Product. The goal of this Data Product is to allow everyone in the company to use Data for analytics.

Most of the times, the first Data Product is done using some sort of ETL like the one below.

A classic ETL pipeline

Let’s take a close look of is happening here:

  • We have a read only replica of the main operational database
  • We fully understand the domain model and schema
  • There is a cron job (or a visual drag and drop tool) that runs a set of MongoDB and SQL queries, applies some transformations and loads it into another SQL database (e.g. another Postgres/MySQL instance because people don’t have time to learn a new database technology.)

The domain is small and its knowledge is shared across all engineering the team, the mapping logic from the operational system to the KPIs is quite simple which makes it quite fast to ship. After the ETL pipeline is done the company is able pull data out of its operational system and has a nice set of dashboards to power its business.

#data-science #data #data-engineering #big-data

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Building Data Platforms — The ETL bias
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Uriah  Dietrich

Uriah Dietrich


What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Virgil  Hagenes

Virgil Hagenes


Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations