Christopher Tozzi

Big Data is defined by the following six features:
1. Highly scalable analytics processes – Big Data platforms have become popular due in large part to their ability to scale. The amount of data that they can analyze without a degradation in performance is virtually unlimited. This is what sets these tools apart from traditional methods of investigating data, such as basic SQL queries.
2. Flexibility – Big Data is flexible data. Whereas in the past all of your data might have been stored in a specific type of database using consistent data structures, today’s datasets come in many forms. Effective analytics strategies are designed to be highly flexible and to handle any type of data that is thrown at them. Fast data transformation is an essential part of Big Data, as is the ability to work with unstructured data.
3. Real-time results – Traditionally, organizations could afford to wait for data analytics results. In the world of Big Data, however, maximizing value means gaining insights in real time. After all, when you are using Big Data for tasks like fraud detection, results received after the fact are of little value.
4. Machine learning applications – Machine learning is not the only way to leverage Big Data. It is, however, an increasingly important application in the Big Data world. Machine learning use cases set Big Data apart from traditional data, which was very rarely used to power machine learning.
5. Scale-out storage systems – Traditionally, data was stored on conventional tape and disk drives. Today, Big Data often relies on software-defined scale-out storage systems that abstract data away from the underlying storage hardware. Of course, not all Big Data is stored on modern storage platforms, which is why the ability to move data quickly between traditional storage and next-generation storage remains important for Big Data applications.
6. Data quality – Data quality is important in any context. With the increasing complexity of Big Data, however, has come greater attention to the importance of ensuring data quality within complex data sets and analytics operations. Attention to data quality is a core feature of any effective Big Data workflow.

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2 Responses to Christopher Tozzi

  1. shinichi says:

    Big Data vs Traditional Data: What Defines Big Data?

    by Christopher Tozzi

    https://blog.syncsort.com/2018/03/big-data/big-data-vs-traditional-data/

    What is Big Data, really? Despite what the term Big Data implies, the definition of Big Data is not actually about the size of your data. It’s how you use the data.

    When it comes to data, size is always relative.

    True, the number of data sources and the amount of information that can be stored and analyzed have increased significantly over the past several years. This increase coincided with the entry of the term Big Data into the popular lexicon.

    Yet it’s not as though enough large data sets didn’t exist until we started talking about Big Data. What we call Big Data today may involve more data than the data sets and workloads of the past, but it may not. Again, it’s all relative.

    What Really Defines Big Data

    If you can’t distinguish Big Data from traditional data sets in terms of size, then what does define Big Data?

    The answer lies in how the data is used. The processes, tools, goals, and strategies that are deployed when working with Big Data are what set Big Data apart from traditional data.

    Specifically, Big Data is defined by the following six features:

    1. Highly scalable analytics processes

    Big Data platforms like Hadoop and Spark have become popular due in large part to their ability to scale. The amount of data that they can analyze without a degradation in performance is virtually unlimited. This is what sets these tools apart from traditional methods of investigating data, such as basic SQL queries. The latter doesn’t scale unless you integrate them into a larger analytics framework.

    2. Flexibility

    Big Data is flexible data. Whereas in the past all of your data might have been stored in a specific type of database using consistent data structures, today’s datasets come in many forms. Effective analytics strategies are designed to be highly flexible and to handle any type of data that is thrown at them. Fast data transformation is an essential part of Big Data, as is the ability to work with unstructured data.

    3. Real-time results

    Traditionally, organizations could afford to wait for data analytics results. In the world of Big Data, however, maximizing value means gaining insights in real time. After all, when you are using Big Data for tasks like fraud detection, results received after the fact are of little value.

    4. Machine learning applications

    Machine learning is not the only way to leverage Big Data. It is, however, an increasingly important application in the Big Data world. Machine learning use cases set Big Data apart from traditional data, which was very rarely used to power machine learning.

    5. Scale-out storage systems

    Traditionally, data was stored on conventional tape and disk drives. Today, Big Data often relies on software-defined scale-out storage systems that abstract data away from the underlying storage hardware. Of course, not all Big Data is stored on modern storage platforms, which is why the ability to move data quickly between traditional storage and next-generation storage remains important for Big Data applications.

    6. Data quality

    Data quality is important in any context. With the increasing complexity of Big Data, however, has come greater attention to the importance of ensuring data quality within complex data sets and analytics operations. Attention to data quality is a core feature of any effective Big Data workflow.

    If you’re not striving to achieve these features in your Big Data, you’re not making the most of your data.

  2. Curtissax says:

    CAB 토토 사이트 홍보를 해 왔던 총판입니다.
    작년12월부터 총판 수익금 526만원을 못받아서
    이렇게 먹튀 사이트에 글을 올리게 됐습니다,
    홍보를 꾸준히 해 오다가 홍보 방법이 막혀서 사전에 CAB운영진의 양해를 구해서
    7-8개월동안 홍보방법을 찾기위해 홍보를 못했습니다,
    그러다 올해 5월부터 홍보방법을 찾아서 홍보를 하다가
    도메인이 유해 사이트로 떠서 새로운 도메인 셋팅을 해 달라고 부탁드렸더니
    충분히 기다릴만큼 기다렸다고 도메인 셋팅을 안해주더군요,,
    홍보 준비하는 기간동안에 수익금은 500만원이 넘어 있었고,나는 정산금보다
    홍보를 계속하고 싶었는데,
    갑자기 도메인 셋팅을 안해준다고 하니,,,황당하더군요
    그래서 도메인 셋팅 안 해 줄거면 정산금을 정산해 달라고 햇더니
    “6개월반이나 기다려 아무성과가 없어서 정산금을 못주겟다네요,,
    그래도 5월에는 많지는 않지만 10여명정도 가입이 있었고,이제 어느정도 효과를 볼려고
    하는데
    정산금 주기 싫어서 같이 일을 못하겟다고 하니,,,이게 먹튀가 아니고 뭔가요?
    내가 이렇게 글을 올리는것도 사전에 CAB운영진에 미리 얘기를 드렸고,마음대로 하라고 하면서
    관리자 비번과 텔레그램까지 차단했고,CAB 사이트에 자세히 알아보니,일반 유저 먹튀도 여러건 발견되어
    이대로 나두면 안되겠다는 생각이 들어 이렇게 글을 올립니다.
    일단 저와 제가 아는 동생이 CAB에서 놀았던 입금 계좌를 경찰에 아는분한테 제출을 할겁니다.
    지금 CAB을 이용하고 계신분이나 이용하실분들은 나중에 통장 계좌가 노출되어 경찰 조사를 받는일이
    없도록 CAB토토사이트 이용을 자제해 주시기 바랍니다,
    그리고 CAB토토 사이트도 상황이 복잡해지면 일반유저 먹튀도 예상되니까,
    가능하면 다른 사이트 이용을 부탁드립니다!!

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