Problem 1

Create an Inverted index. Given a set of documents, an inverted index is a dictionary where each word is associated with a list of the document identifiers in which that word appears.

Mapper Input

The input is a 2-element list: [document_id, text], where document_id is a string representing a document identifier and text is a string representing the text of the document. The document text may have words in upper or lower case and may contain punctuation. You should treat each token as if it was a valid word; that is, you can just use value.split() to tokenize the string.

Reducer Output

The output should be a (word, document ID list) tuple where word is a String and document ID list is a list of Strings.

You can test your solution to this problem using books.json:

1

     python inverted_index.py books.json

You can verify your solution against inverted_index.json.

Answer:

import MapReduce

import json

import sys

# Part 1

mr = MapReduce.MapReduce()

# Part 2

def mapper(record):

    for word in record[1].split():

        mr.emit_intermediate(word, record[0])

# Part 3

def reducer(key, list_of_values):

    mr.emit((key, list_of_values))

# Part 4

inputdata = open(sys.argv[1])

mr.execute(inputdata, mapper, reducer)

Sample Output:

["all", ["milton-paradise.txt", "blake-poems.txt", "melville-moby_dick.txt"]]

["Rossmore", ["edgeworth-parents.txt"]]

["Consumptive", ["melville-moby_dick.txt"]]

["forbidden", ["milton-paradise.txt"]]

["child", ["blake-poems.txt"]]

 Problem statement:

Assume you have two matrices A and B in a sparse matrix format, where each record is of the form i, j, value. Design a MapReduce algorithm to compute the matrix multiplication A x B

Map Input

The input to the map function will be a row of a matrix represented as a list. Each list will be of the form [matrix, i, j, value] where matrix is a string and i, j, and value are integers.

The first item, matrix, is a string that identifies which matrix the record originates from. This field has two possible values: "a" indicates that the record is from matrix A and "b" indicates that the record is from matrix B.

Reduce Output

The output from the reduce function will also be a row of the result matrix represented as a tuple. Each tuple will be of the form (i, j, value) where each element is an integer.

Answer:

#!/usr/bin/python

import MapReduce

import json

import sys

# Part 1

mr = MapReduce.MapReduce()

# A has dimensions L,M

# B has dimensions M,N

L = 5

M = 5

N = 5

# Part 2

def mapper(record):

    print(f"record={record} \t + {record[0]} + \t + {record[1]} + \t + {record[2]} + \t + {record[3]}")

    matrix_index  = record[0]

    row           = record[1]

    col           = record[2]

    value         = record[3]

    if matrix_index == "a":

        for i in range(0, N):

            key = f"{row},{i}"

            mr.emit_intermediate(key, ("a", row, col, value))

    if matrix_index == "b":

        for j in range(0, L):

            key = f"{j},{col}"

            mr.emit_intermediate(key, ("b", row, col, value))

# Part 3

def reducer(key, list_of_values):

    # one reducer per output cell of destination matrix

    # print(f"{key},{list_of_values}")

    total = 0

    line = ""

    for k in range(0,M):

        left = getcolumn(list_of_values, k, "a")

        right = getrow(list_of_values, k, "b")

        total += left*right

        line += f"{left}*{right}={left*right} +"

    line += f"= {total}"

    print(line)

    mr.emit((int(key.split(',')[0]), int(key.split(',')[1]), total))

def getcolumn(values, k, matrix_type):

    result = 0

    for item in values:

        mtype = item[0]

        row = item[1]

        col = item[2]

        value = item[3]

        if mtype == matrix_type and col == k:

           result = value

           break

    return result

def getrow(values, k, matrix_type):

    result = 0

    for item in values:

        mtype = item[0]

        row = item[1]

        col = item[2]

        value = item[3]

        if matrix_type == mtype and row == k:

           result = value

           break

    return result

# Part 4

inputdata = open(sys.argv[1])

mr.execute(inputdata, mapper, reducer)

 

Output:

python3 multiply1.py matrix.json

record=['a', 0, 0, 63]   + a +   + 0 +   + 0 +   + 63

record=['a', 0, 1, 45]   + a +   + 0 +   + 1 +   + 45

record=['a', 0, 2, 93]   + a +   + 0 +   + 2 +   + 93

record=['a', 0, 3, 32]   + a +   + 0 +   + 3 +   + 32

record=['a', 0, 4, 49]   + a +   + 0 +   + 4 +   + 49

record=['a', 1, 0, 33]   + a +   + 1 +   + 0 +   + 33

record=['a', 1, 3, 26]   + a +   + 1 +   + 3 +   + 26

record=['a', 1, 4, 95]   + a +   + 1 +   + 4 +   + 95

record=['a', 2, 0, 25]   + a +   + 2 +   + 0 +   + 25

record=['a', 2, 1, 11]   + a +   + 2 +   + 1 +   + 11

record=['a', 2, 3, 60]   + a +   + 2 +   + 3 +   + 60

record=['a', 2, 4, 89]   + a +   + 2 +   + 4 +   + 89

record=['a', 3, 0, 24]   + a +   + 3 +   + 0 +   + 24

record=['a', 3, 1, 79]   + a +   + 3 +   + 1 +   + 79

record=['a', 3, 2, 24]   + a +   + 3 +   + 2 +   + 24

record=['a', 3, 3, 47]   + a +   + 3 +   + 3 +   + 47

record=['a', 3, 4, 18]   + a +   + 3 +   + 4 +   + 18

record=['a', 4, 0, 7]    + a +   + 4 +   + 0 +   + 7

record=['a', 4, 1, 98]   + a +   + 4 +   + 1 +   + 98

record=['a', 4, 2, 96]   + a +   + 4 +   + 2 +   + 96

record=['a', 4, 3, 27]   + a +   + 4 +   + 3 +   + 27

record=['b', 0, 0, 63]   + b +   + 0 +   + 0 +   + 63

record=['b', 0, 1, 18]   + b +   + 0 +   + 1 +   + 18

record=['b', 0, 2, 89]   + b +   + 0 +   + 2 +   + 89

record=['b', 0, 3, 28]   + b +   + 0 +   + 3 +   + 28

record=['b', 0, 4, 39]   + b +   + 0 +   + 4 +   + 39

record=['b', 1, 0, 59]   + b +   + 1 +   + 0 +   + 59

record=['b', 1, 1, 76]   + b +   + 1 +   + 1 +   + 76

record=['b', 1, 2, 34]   + b +   + 1 +   + 2 +   + 34

record=['b', 1, 3, 12]   + b +   + 1 +   + 3 +   + 12

record=['b', 1, 4, 6]    + b +   + 1 +   + 4 +   + 6

record=['b', 2, 0, 30]   + b +   + 2 +   + 0 +   + 30

record=['b', 2, 1, 52]   + b +   + 2 +   + 1 +   + 52

record=['b', 2, 2, 49]   + b +   + 2 +   + 2 +   + 49

record=['b', 2, 3, 3]    + b +   + 2 +   + 3 +   + 3

record=['b', 2, 4, 95]   + b +   + 2 +   + 4 +   + 95

record=['b', 3, 0, 77]   + b +   + 3 +   + 0 +   + 77

record=['b', 3, 1, 75]   + b +   + 3 +   + 1 +   + 75

record=['b', 3, 2, 85]   + b +   + 3 +   + 2 +   + 85

record=['b', 4, 1, 46]   + b +   + 4 +   + 1 +   + 46

record=['b', 4, 2, 33]   + b +   + 4 +   + 2 +   + 33

record=['b', 4, 3, 69]   + b +   + 4 +   + 3 +   + 69

record=['b', 4, 4, 88]   + b +   + 4 +   + 4 +   + 88

63*63=3969 +45*59=2655 +93*30=2790 +32*77=2464 +49*0=0 += 11878

63*18=1134 +45*76=3420 +93*52=4836 +32*75=2400 +49*46=2254 += 14044

63*89=5607 +45*34=1530 +93*49=4557 +32*85=2720 +49*33=1617 += 16031

63*28=1764 +45*12=540 +93*3=279 +32*0=0 +49*69=3381 += 5964

63*39=2457 +45*6=270 +93*95=8835 +32*0=0 +49*88=4312 += 15874

33*63=2079 +0*59=0 +0*30=0 +26*77=2002 +95*0=0 += 4081

33*18=594 +0*76=0 +0*52=0 +26*75=1950 +95*46=4370 += 6914

33*89=2937 +0*34=0 +0*49=0 +26*85=2210 +95*33=3135 += 8282

33*28=924 +0*12=0 +0*3=0 +26*0=0 +95*69=6555 += 7479

33*39=1287 +0*6=0 +0*95=0 +26*0=0 +95*88=8360 += 9647

25*63=1575 +11*59=649 +0*30=0 +60*77=4620 +89*0=0 += 6844

25*18=450 +11*76=836 +0*52=0 +60*75=4500 +89*46=4094 += 9880

25*89=2225 +11*34=374 +0*49=0 +60*85=5100 +89*33=2937 += 10636

25*28=700 +11*12=132 +0*3=0 +60*0=0 +89*69=6141 += 6973

25*39=975 +11*6=66 +0*95=0 +60*0=0 +89*88=7832 += 8873

24*63=1512 +79*59=4661 +24*30=720 +47*77=3619 +18*0=0 += 10512

24*18=432 +79*76=6004 +24*52=1248 +47*75=3525 +18*46=828 += 12037

24*89=2136 +79*34=2686 +24*49=1176 +47*85=3995 +18*33=594 += 10587

24*28=672 +79*12=948 +24*3=72 +47*0=0 +18*69=1242 += 2934

24*39=936 +79*6=474 +24*95=2280 +47*0=0 +18*88=1584 += 5274

7*63=441 +98*59=5782 +96*30=2880 +27*77=2079 +0*0=0 += 11182

7*18=126 +98*76=7448 +96*52=4992 +27*75=2025 +0*46=0 += 14591

7*89=623 +98*34=3332 +96*49=4704 +27*85=2295 +0*33=0 += 10954

7*28=196 +98*12=1176 +96*3=288 +27*0=0 +0*69=0 += 1660

7*39=273 +98*6=588 +96*95=9120 +27*0=0 +0*88=0 += 9981

[0, 0, 11878]

[0, 1, 14044]

[0, 2, 16031]

[0, 3, 5964]

[0, 4, 15874]

[1, 0, 4081]

[1, 1, 6914]

[1, 2, 8282]

[1, 3, 7479]

[1, 4, 9647]

[2, 0, 6844]

[2, 1, 9880]

[2, 2, 10636]

[2, 3, 6973]

[2, 4, 8873]

[3, 0, 10512]

[3, 1, 12037]

[3, 2, 10587]

[3, 3, 2934]

[3, 4, 5274]

[4, 0, 11182]

[4, 1, 14591]

[4, 2, 10954]

[4, 3, 1660]

[4, 4, 9981]

#codingexercise: https://1drv.ms/w/s!Ashlm-Nw-wnWhM0bmlY_ggTBTNTYxQ?e=s7hP7W

 Given that tweets have location, find the happiest state:

Answer: happiest_state.py:

import sys

def hw():

    afinnfile = open("AFINN-111.txt")

    scores = {} # initialize an empty dictionary

    for line in afinnfile:

      term, score  = line.split("\t")  # The file is tab-delimited. "\t" means "tab character"

      scores[term] = int(score)  # Convert the score to an integer.

    print scores.items()

    import json

    outputfile = open("output.txt")

    tweets = []

    for line in outputfile:

      tweets += [json.loads(line)]

    nonsentiment_scores = []

    for item in tweets:

        if item.text:

           sentence = trim(item.text)

           words = sentence.split()

           score = 0

           for i, word in enumerate(words, start=1):

               term = tolower(trim(word))

               if term not in scores:

                  if i-1 > 0 && is_present(scores, words[i-1]):

                     score += get_score(scores, words[i-1]) > 0 ? 1 : -1

                  if i+1 < len(words) && is_present(scores,words[i+1]):

                     score += get_score(scores, words[i-1]) > 0 ? 1 : -1

                  score = score/3

           nonsentiment_scores.append(tolower(trim(word)), score)

   for item in nonsentiment_scores:

       print(item)

def is_present(scores, word):

     term = tolower(trim(word))

     return term in scores

def get_score(scores, word):

    score = 0

    term = tolower(trim(word))

    if term in scores:

       if scores[term] > 0:

          score += 1

       else if scores[term] < 0:

          score -= 1

       else:

          score += 0

    return score

def lines(fp):

    print str(len(fp.readlines()))

states = {

        'AK': 'Alaska',

        'AL': 'Alabama',

        'AR': 'Arkansas',

        'AS': 'American Samoa',

        'AZ': 'Arizona',

        'CA': 'California',

        'CO': 'Colorado',

        'CT': 'Connecticut',

        'DC': 'District of Columbia',

        'DE': 'Delaware',

        'FL': 'Florida',

        'GA': 'Georgia',

        'GU': 'Guam',

        'HI': 'Hawaii',

        'IA': 'Iowa',

        'ID': 'Idaho',

        'IL': 'Illinois',

        'IN': 'Indiana',

        'KS': 'Kansas',

        'KY': 'Kentucky',

        'LA': 'Louisiana',

        'MA': 'Massachusetts',

        'MD': 'Maryland',

        'ME': 'Maine',

        'MI': 'Michigan',

        'MN': 'Minnesota',

        'MO': 'Missouri',

        'MP': 'Northern Mariana Islands',

        'MS': 'Mississippi',

        'MT': 'Montana',

        'NA': 'National',

        'NC': 'North Carolina',

        'ND': 'North Dakota',

        'NE': 'Nebraska',

        'NH': 'New Hampshire',

        'NJ': 'New Jersey',

        'NM': 'New Mexico',

        'NV': 'Nevada',

        'NY': 'New York',

        'OH': 'Ohio',

        'OK': 'Oklahoma',

        'OR': 'Oregon',

        'PA': 'Pennsylvania',

        'PR': 'Puerto Rico',

        'RI': 'Rhode Island',

        'SC': 'South Carolina',

        'SD': 'South Dakota',

        'TN': 'Tennessee',

        'TX': 'Texas',

        'UT': 'Utah',

        'VA': 'Virginia',

        'VI': 'Virgin Islands',

        'VT': 'Vermont',

        'WA': 'Washington',

        'WI': 'Wisconsin',

        'WV': 'West Virginia',

        'WY': 'Wyoming'

}

def main():

    sent_file = open(sys.argv[1])

    tweet_file = open(sys.argv[2])

    hw()

    lines(sent_file)

    lines(tweet_file)

if __name__ == '__main__':

    main()

 Tweet sentiment analyzer:

import sys

def hw():

    afinnfile = open("AFINN-111.txt")

    scores = {} # initialize an empty dictionary

    for line in afinnfile:

      term, score  = line.split("\t")  # The file is tab-delimited. "\t" means "tab character"

      scores[term] = int(score)  # Convert the score to an integer.

    print scores.items()

    import json

    outputfile = open("output.txt")

    tweets = []

    for line in outputfile:

      tweets += [json.loads(line)]

    for item in tweets:

        if item.text:

           sentence = trim(item.text)

           words = sentence.split()

           score = 0

           for word in words:

               term = tolower(trim(word))

               if term in scores:

                   if scores[term] > 0:

                      score += 1

                   else if scores[term] < 0:

                      score -= 1

                   else:

                      score += 0

           if len(words) > 0:

              score = score/len(words)

           print(score)

        else:

           print(0)

def lines(fp):

    print str(len(fp.readlines()))

def main():

    sent_file = open(sys.argv[1])

    tweet_file = open(sys.argv[2])

    hw()

    lines(sent_file)

    lines(tweet_file)

if __name__ == '__main__':

    main()

 This is a continuation of previous articles on Azure resources, their IaC deployments and trends in data infrastructure. The previous article touched upon data platforms and how they go out of their way to recommend that data must not be given to vendors and not even to the platform and that it is proprietary. This section continues that line of discussion to elaborate on understanding data.

The role of data in modern business operations is changing, with organizations facing the challenge of harnessing their potential and safeguarding it with utmost care. Data governance is crucial for businesses to ensure the protection, governance, and effective management of their data assets. Compliance frameworks like the EU's AI Act highlight the importance of maintaining high-quality data for successful AI integration and utilization.

The complex web of data governance presents multifaceted challenges, especially in the realm of data silos and disparate governance mechanisms. Tracking data provenance, ensuring data visibility, and implementing robust protection schemes are crucial for mitigating cybersecurity risks and ensuring data integrity across various platforms and applications.

The evolution of artificial intelligence (AI) introduces new dimensions to data management practices, as organizations explore the transformative potential of AI and machine learning technologies. Leveraging AI for tasks like backup recovery, compliance, and data protection plans offers unprecedented opportunities for enhancing operational efficiencies and driving innovation within businesses.

The future of data management lies at the intersection of compliance, resilience, security, backup, recovery, and AI integration. By embracing these foundational pillars, businesses can navigate the intricate landscape of data governance with agility and foresight, paving the way for sustainable data-driven strategies and robust cybersecurity protocols.

Prioritizing data management practices that align with compliance standards and cybersecurity best practices is key. By embracing the transformative potential of AI while maintaining a steadfast commitment to data protection, businesses can navigate the complexities of the digital landscape with confidence and resilience.

References:

Previous article explaining a catalog: IaCResolutionsPart148.docx

https://docs.databricks.com/en/data-governance/unity-catalog/enable-workspaces.html#enable-workspace 

https://docs.databricks.com/en/data-governance/unity-catalog/create-metastore.html

#codingexercise: https://1drv.ms/w/s!Ashlm-Nw-wnWhPIMgfH3QDAPfwCW6Q?e=dM89NH

 The shift from dbms to catalogs is already underway. Earlier, the databases were the veritable access grantors but with heterogenous data stores, this has shifted to catalogs like the Unity Catalog for databricks and the Horizon catalog for Snowflake. This is a deliberate attempt from the perspective of these platforms even though they fight for their ecosystems. The end-users and the organizations that empower them are rapidly making this shift themselves. 

For example, the Databricks Unity Catalog offers centralized access control, auditing, lineage, and data discovery capabilities across multiple Databricks workspaces. It includes user management, metastore, clusters, SQL warehouses, and a standards-compliant security model based on ANSI SQL. The catalog also includes built-in auditing and lineage, allowing for user-level audit logs and data discovery. The metadata store is a top-level container, while the data catalog has a three-level namespace namely catalog.schema.table. The catalog explorer allows for creation of tables and views, while the tables of views and volumes provide governance for nontabular data. The catalog is multi-cloud friendly, allowing for federation across multiple cloud vendors and unified access. The idea here is that you can define once and secure anywhere. 

Databricks Unity Catalog consists of a metastore and a catalog. The metastore is the top-level logical container for metadata, storing data assets like tables or models and defining the namespace hierarchy. It handles access control policies and auditing. The catalog is the first-level organizational unit within the metastore, grouping related data assets and providing access controls. However, only one metastore per deployment is used. Each Databricks region requires its own Unity Catalog metastore.  

There is a Unity catalog quick start notebook in Python. The key steps include creating a workspace with the Unity Catalog meta store, creating a catalog, creating a managed schema, managing a table, and using the Unity catalog in the Pandas API on Spark. The code starts with creating a catalog, selecting show, and then creating a managed schema. The next step involves creating and managing schemas, extending them, and granting permissions. The table is managed using the schema created earlier, and the table is shown and all available tables are shown. The final step involves using the Pandas API on Spark, which can be found in the official documentation for Databricks. This quick start is a great way to get a feel for the process and to toggle back and forth with the key steps inside the code. 

The Unity Catalog system employs object security best practices, including access control lists (ACLs) for granting or restricting access to specific users and groups on securable objects. ACLs provide fine-grain control, ensuring access to sensitive data and objects. Less privilege is used, limiting access to the minimum required, avoiding broad groups like All Users unless necessary. Access is revoked once the purpose is served, and policies are reviewed regularly for relevance. This technique enhances data security and compliance, prevents unnecessary broad access, and controls a blast radius in case of security breaches. 

The Databricks Unity Catalog system offers best practices for catalogs. First, create a separate catalog for loose coupling, managing access and compliance at the catalog level. Align catalog boundaries with business domains or applications, such as marketing analytics or HR. Customize security policies and governance within the catalog to drill down into specific domains. Create access control groups and roles specific to a catalog, fine-tune read-write privileges, and customize settings like resource quotas and scrum rules. These fine-grain policies provide the best of security and functionality in catalogs. 

To ensure security and manage external connections, limit visibility by granting access only to specific users, groups, and roles, and setting lease privileges. Limit access to only necessary users and groups using granular access control lists or ACLs. Be aware of team activities and avoid giving them unnecessary access to external resources. Tag connections effectively for discovery using source categories or data classifications, and discover connections by use case for organizational visibility. This approach enhances security, prevents unintended data access, and simplifies external connection discovery and management. 

Databricks Unity Catalog Business Unit Best Practices emphasize the importance of providing dedicated sandboxes for each business unit, allowing independent development environments, and preventing interference between different workflows. Centralizing shareable data into production catalogs ensures consistency and reduces the need for duplicate data. Discoverability is crucial, with meaningful naming conventions and metadata best practices. Federated queries via Lakehouse architecture unify data access across silos, governing securely via contracts and permissions. This approach supports autonomy for units, increases productivity through reuse, and maintains consistency with collaborative governance. This approach supports autonomy, increases productivity, and maintains consistency. 

In conclusion, the Unity catalog standard allows centralized data governance and best practices for catalogs, connections, and business units. 

https://docs.databricks.com/en/data-governance/unity-catalog/enable-workspaces.html#enable-workspace 

https://docs.databricks.com/en/data-governance/unity-catalog/create-metastore.html

 This is a summary of the book titled “Active Listening Techniques – 30 Practical tools to hone your Communication Skills” written by Nixaly Leonardo and published by Callisto in 2020. The author offers insights into active listening building off a decade of social work. She covers listening skills such as mindfulness, empathy, non-verbal cues, and effective questioning techniques – all of which lead to a deeper understanding of others. Her five-point agenda includes empathizing with others before interacting, being aware of the tensions so as to respond not react, acknowledging one’s negative emotions, involving loved ones in the journey, writing journal entries about our reactions and being aware of this emotional state. This will help us adjust our communications and persuading others by acknowledging their needs, projecting confidence, and choosing the right words, dealing with stressful situations by validating other people’s emotions, easing tension, and refocusing the conversations.

Active listening is a crucial communication skill that involves paying attention, understanding people's emotions, and giving time for others to talk. It is applied in various situations, including work, personal relationships, and therapy. Active listening helps individuals feel supported and heard, and it demonstrates respect for others. To improve communication skills, seven fundamentals can be applied: paraphrasing, using nonverbal language, emotional labeling, silence, redirection, mirroring, and validating.

Paraphrasing involves restating what someone says to ensure understanding, while nonverbal cues like eye contact, gestures, posture, and facial expressions help convey the message. Emotional labeling involves noticing and repeating what others feel, while silence allows for time to think and express thoughts without interruption. Redirecting the conversation back to the original topic helps maintain direction and reduce tension. Mirroring involves faking the speaker's body language and tone of voice to create a sense of connection and rapport. Validating others' emotions allows them to experience their emotions and hold their beliefs, making them feel understood and supported.

Active listening involves being present and mindful during conversations, ignoring distractions and staying open-minded. It helps us accept that we all experience negative emotions and stress and understand how our experiences shape our perceptions and interpretations of others' messages. To challenge and move through assumptions, empathize with others, be aware of tension, apologize when you react negatively, involve loved ones, and write journal entries about your reactions.

Be aware of your emotional state during conversations, as strong emotions can interfere with attentive listening. Adjust your communication to ensure others hear and understand you, considering other people's communication styles and preferences. Navigate situations tactfully by asking questions instead of directly challenging your supervisor's idea, describing or praise their vision, and seeking details to address your concerns without undermining their creativity or judgment.

Know your audience wisely, choosing when and where to raise critical issues and choosing the appropriate mode of communication. Electronic communication such as texting and email can be more effective than face-to-face conversations. By following these steps, you can become a better active listener and maintain a productive dialogue.

Persuasion involves acknowledging others' needs, projecting confidence, and choosing the right words. It is a matter of giving and taking and understanding why someone might not agree with your viewpoint is crucial. Acknowledging their needs helps build respect and build a stronger bond. Using precise language is essential in handling sensitive situations, avoiding hurting others and conveying your intended message. Confidence is key, so pretending to be confident can help.

To deal with stressful situations, validate others' emotions, easing tension, and refocusing the conversation. Addressing emotional concerns fosters stronger connections and genuine conversations. Calming others can ease tensions by recognizing escalating situations, lowering your tone, seeking clarification, taking responsibility for your contribution, and addressing the speaker's concerns. If tensions continue to rise, repeat the steps, or suggest a break. Set boundaries and communicate potential consequences if the conversation escalates. 

When a conversation goes awry, refocus on the original subject to avoid defensiveness and avoid resolving the issue. Address communication challenges by rephrasing statements, acknowledging shifts, asking for thoughts, and validating the listener's feelings. This ensures both parties hear and understand each other, preventing a recurrence of arguments. By following these steps, you can ensure effective communication.

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