LookML BI for data analysts, engineers, CDOs

Business intelligence workflows at any organization can help the business make better decisions, like where to expand your company and how to most effectively deploy your resources. My work as a data analyst, open data advocate, and former lead of the Google Cloud Public Datasets Program has given me a broad view into how data teams develop their business intelligence workflows. I’ve been fortunate to work with data analysis teams across numerous industries, including retail, weather, financial services, and more. Throughout this, I’ve seen the common challenges that teams run into in their business intelligence workflows.

You might think that teams’ challenges stem from the tool they pick, but every business intelligence tool has its own advantages. Some excel at data visualization, others are great for sharing dashboards, and some BI tools do well with data preparation. Most BI tools connect easily to at least some data warehouses, and have the ability to visualize these data.

But every BI tool also has its drawbacks. And because every team across an enterprise has slightly different requirements for BI, they often choose different tools, creating a segmentation problem within a company. The most common form of this I’ve seen is that metrics are defined differently within each tool and there is no centralized data governance, which leads to unnecessarily duplicated workflows across the company.

Looker, now part of Google Cloud, can help address these “in-between product” issues. LookML, Looker’s powerful semantic modeling layer, gives teams the ability to easily create a standardized data governance structure and empowers users across the enterprise to undertake their own analysis while trusting that they all are built on the same single source of truth. (You can read more about why Looker developed LookML in this blog post.)

In this post, though, we’ll focus on five groups who can benefit from LookML and see how it can simplify their BI workflows. For each group, you’ll see how LookML can help, with a snippet of LookML code as an example. Click through to the “Here’s an example” link to GitHub repositories to see the full LookML file if you’d like more detail.

Data engineers and modelers

Who are you : You are the group that most obviously benefits from LookML. Your title is probably “business intelligence analyst” or “data engineer.” Your team builds the underlying infrastructure that makes data-driven decision-making possible, standardizes the data that feeds key metrics, and helps measure progress toward KPIs. How LookML helps: LookML is all about reusability. It brings to data modeling many of the tools and methodologies used in software development, such as collaborative development with Git integration, object definitions, and inheritance. It allows you to define a dimension or measure once and build on it, instead of having to repeat this effort. This enables you to standardize metrics and the data that define them across the entire enterprise in a scalable manner that saves you time. By converting raw data into meaningful metrics using LookML, you empower BI users across the entire enterprise, from accounting to marketing, to easily get started building their dashboards with the confidence that comes from knowing their metrics are properly defined and aggregated.

Here’s an example: One common challenge for businesses is being able to compare profit and margin across different business units because of differences in revenue sources, inventory costs, personnel expenses, and other factors. This often leaves decision-makers siloed from each other and requires you to make manual adjustments any time you want to do cross-silo comparisons.

However, LookML can eliminate that challenge. The LookML snippet below joins the item cost from the inventory_items table with the sale price from the order_items table, so that gross_margin can be defined as sales_price - inventory_items.cost. Once that’s in place, you can see how easily gross_margin is referenced repeatedly throughout other dimension definitions. You can call ${gross_margin} without having to either rewrite the SQL each time or rerun the same SQL statement in several different places.

#google cloud platform #data analytics #data analytic

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LookML BI for data analysts, engineers, CDOs
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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

Data Scientist, Data Engineer & Other Data Careers, Explained

The data-related career landscape can be confusing, not only to newcomers, but also to those who have spent time working within the field.

Get in where you fit in. Focusing on newcomers, however, I find from requests that I receive from those interested in join the data field in some capacity that there is often (and rightly) a general lack of understanding of what it is one needs to know in order to decide where it is that they fit in. In this article, we will have a look at five distinct data career archetypes, and hopefully provide some advice on how to get one’s feet wet in this vast, convoluted field.

We will focus solely on industry roles, as opposed to those in research, as not to add an additional layer of complication. We will also omit executive level positions such as Chief Data Officer and the like, mostly because if you are at the point in your career that this role is an option for you, you probably don’t need the information in this article.

So here are 5 data career archetypes, replete with descriptions and information on what makes them distinct from one another.

Data Architect

The data architect focuses on engineering and managing data stores and the data that reside within them.

The data architect is concerned with managing data and engineering the infrastructure which stores and supports this data. There is generally little to no data analysis needing to take place in such a role (beyond data store analysis for performance tuning), and the use of languages such as Python and R is likely not necessary. An expert level knowledge of relational and non-relational databases, however, will undoubtedly be necessary for such a role. Selecting data stores for the appropriate types of data being stored, as well as transforming and loading the data, will be necessary. Databases, data warehouses, and data lakes; these are among the storage landscapes that will be in the data architect’s wheelhouse. This role is likely the one which will have the greatest understanding of and closest relationship with hardware, primarily that related to storage, and will probably have the best understanding of cloud computing architectures of anyone else in this article as well.

SQL and other data query languages — such as Jaql, Hive, Pig, etc. — will be invaluable, and will likely be some of the main tools of an ongoing data architect’s daily work after a data infrastructure has been designed and implemented. Verifying the consistency of this data as well as optimizing access to it are also important tasks for this role. A data architect will have the know-how to maintain appropriate data access rights, ensure the infrastructure’s stability, and guarantee the availability of the housed data.

This is differentiated from the data engineer role by focus: while a data engineer is concerned with building and maintaining data pipelines (see below), the data architect is focused on the data itself. There may be overlap between the 2 roles, however: ETL; any task which could transform or move data, especially from one store to another; starting data on a journey down a pipeline.

Like other roles in this article, you might not necessarily see a “data architect” role advertised as such, and might instead see related job titles, such as:

  • Database Administrator
  • Spark Administrator
  • Big Data Administrator
  • Database Engineer
  • Data Manager

Data Engineer

The data engineer focuses on engineering and managing the infrastructure which supports the data and data pipelines.

What is the data infrastructure? It’s the collection of software and storage solutions that allow for the retrieval of data from a data store, the processing of data in some specified manner (or series of manners), the movement of data between tasks (as well as the tasks themselves), as data is on its way to analysis or modeling, as well as the tasks which come after this analysis or modeling. It’s the pathway that the data takes as it moves along its journey from its home to its ultimate location of usefulness, and beyond. The data engineer is certainly familiar with DataOps and its integration into the data lifecycle.

From where does the data infrastructure come? Well, it needs to be designed and implemented, and the data engineer does this. If the data architect is the automobile mechanic, keeping the car running optimally, then data engineering can be thought of as designing the roadway and service centers that the automobile requires to both get around and to make the changes needed to continue on the next section of its journey. The pair of these roles are crucial to both the functioning and movement of your automobile, and are of equal importance when you are driving from point A to point B.

Truth be told, some the technologies and skills required for data engineering and data management are similar; however, the practitioners of these disciplines use and understand these concepts at different levels. The data engineer may have a foundational knowledge of securing data access in a relational database, while the data architect has expert level knowledge; the data architect may have some understanding of the transformation process that an organization requires its stored data to undergo prior to a data scientist performing modeling with that data, while a data engineer knows this transformation process intimately. These roles speak their own languages, but these languages are more or less mutually intelligible.

#data analyst #data engineer #data engineering #data management #data science

Gerhard  Brink

Gerhard Brink

1621413060

Top 5 Exciting Data Engineering Projects & Ideas For Beginners [2021]

Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it.

You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience.

Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.

That’s because you’ll need to complete the projects correctly. Here are the most important ones:

  • Python and its use in big data
  • Extract Transform Load (ETL) solutions
  • Hadoop and related big data technologies
  • Concept of data pipelines
  • Apache Airflow

#big data #big data projects #data engineer #data engineer project #data engineering projects #data projects

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10 Must-have Skills for Data Engineering Jobs

Big data skills are crucial to land up data engineering job roles. From designing, creating, building, and maintaining data pipelines to collating raw data from various sources and ensuring performance optimization, data engineering professionals carry a plethora of tasks. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and to name a few.

Here, we list some of the important skills that one should possess to build a successful career in big data.

1. Database Tools
2. Data Transformation Tools
3. Data Ingestion Tools
4. Data Mining Tools

#big data #latest news #data engineering jobs #skills for data engineering jobs #10 must-have skills for data engineering jobs #data engineering

Gerhard  Brink

Gerhard Brink

1620629020

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.

Introduction

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