Bailee  Streich

Bailee Streich

1621957380

Exploring the Fundamentals Binary Serialized Data Structures

In this article, we’ll study how to operate and leverage binary serialized data structures for effective and efficient data utilization.

There are a great many binary formats that data might live in. Everything very popular has grown good open-source libraries, but you may encounter some legacy or in-house format for which this is not true. Good general advice is that unless there is an ongoing and/or performance-sensitive need for processing an unusual format, try to leverage existing parsers. Custom formats can be tricky, and if one is uncommon, it is as likely as not also to be under-documented.

_ This article is an excerpt from the book, Cleaning Data for Effective Data Science—a comprehensive guide for data scientists to master effective data cleaning tools and techniques in a language-agnostic manner. _

If an existing tool is only available in a language you do not wish to use for your main data science work, nonetheless see if that can be easily leveraged to act only as a means to export to a more easily accessed format. A fire-and-forget tool might be all you need, even if it is one that runs recurringly, but asynchronously with the actual data processing you need to perform.

For this article section, let us assume that the optimistic situation is not realized, and we have nothing beyond some bytes on disk, and some possibly flawed documentation to work with. Writing the custom code is much more the job of a systems engineer than a data scientist, but we data scientists need to be polymaths, and we should not be daunted by writing a little bit of systems code.

Here, we look at a simple and straightforward binary format. Moreover, this is a real-world data format for which we do not actually need a custom parser. Having an actual well-tested, performant, and bullet-proof parser to compare our toy code with is a good way to make sure we do the right thing. Specifically, we will read data stored in the NumPy NPY format, which is documented as follows (abridged):

  • The first 6 bytes are a magic string: exactly \x93NUMPY.
  • The next 1 byte is an unsigned byte: the major version number of the file format, e.g. \x01.
  • The next 1 byte is an unsigned byte: the minor version number of the file format, e.g. \x00.
  • The next 2 bytes form a little-endian unsigned short int: the length of the header data HEADER_LEN.
  • The next HEADER_LEN bytes are an ASCII string that contains a Python literal expression of a dictionary.
  • Following the header comes the array data.

#python #data science #data #code #data structure #array #numpy #data cleaning #binary #struct

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Exploring the Fundamentals Binary Serialized Data Structures
Bailee  Streich

Bailee Streich

1621957380

Exploring the Fundamentals Binary Serialized Data Structures

In this article, we’ll study how to operate and leverage binary serialized data structures for effective and efficient data utilization.

There are a great many binary formats that data might live in. Everything very popular has grown good open-source libraries, but you may encounter some legacy or in-house format for which this is not true. Good general advice is that unless there is an ongoing and/or performance-sensitive need for processing an unusual format, try to leverage existing parsers. Custom formats can be tricky, and if one is uncommon, it is as likely as not also to be under-documented.

_ This article is an excerpt from the book, Cleaning Data for Effective Data Science—a comprehensive guide for data scientists to master effective data cleaning tools and techniques in a language-agnostic manner. _

If an existing tool is only available in a language you do not wish to use for your main data science work, nonetheless see if that can be easily leveraged to act only as a means to export to a more easily accessed format. A fire-and-forget tool might be all you need, even if it is one that runs recurringly, but asynchronously with the actual data processing you need to perform.

For this article section, let us assume that the optimistic situation is not realized, and we have nothing beyond some bytes on disk, and some possibly flawed documentation to work with. Writing the custom code is much more the job of a systems engineer than a data scientist, but we data scientists need to be polymaths, and we should not be daunted by writing a little bit of systems code.

Here, we look at a simple and straightforward binary format. Moreover, this is a real-world data format for which we do not actually need a custom parser. Having an actual well-tested, performant, and bullet-proof parser to compare our toy code with is a good way to make sure we do the right thing. Specifically, we will read data stored in the NumPy NPY format, which is documented as follows (abridged):

  • The first 6 bytes are a magic string: exactly \x93NUMPY.
  • The next 1 byte is an unsigned byte: the major version number of the file format, e.g. \x01.
  • The next 1 byte is an unsigned byte: the minor version number of the file format, e.g. \x00.
  • The next 2 bytes form a little-endian unsigned short int: the length of the header data HEADER_LEN.
  • The next HEADER_LEN bytes are an ASCII string that contains a Python literal expression of a dictionary.
  • Following the header comes the array data.

#python #data science #data #code #data structure #array #numpy #data cleaning #binary #struct

Siphiwe  Nair

Siphiwe Nair

1620466520

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

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

Cyrus  Kreiger

Cyrus Kreiger

1617959340

4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company

Cyrus  Kreiger

Cyrus Kreiger

1618039260

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